[upstream-sync] .github/workflows/pr-skill-scan.yml from K-Dense-AI/scientific-agent-skills@9c9bd2e9 [unknown]

Merge pull request '[Upstream sync] yboulaamane/awesome-drug-discovery (github) — 2 added, 0 modified' (#5 ) from upstream-sync/awesome-drug-discovery-20260626-b8fbd7 into main
Reviewed-on: #5
2026-06-26 20:34:47 +00:00 · 2026-06-26 20:13:47 +00:00 · 2026-06-26 20:13:27 +00:00 · 2026-06-26 20:13:05 +00:00 · 2026-06-26 20:12:37 +00:00 · 2026-06-26 19:59:44 +00:00
16 changed files with 2214 additions and 2 deletions
@@ -1,3 +1,22 @@
-# drug-discovery-prompts
+# Drug Discovery AI Prompts

-Curated AI prompts for drug discovery: target identification, SAR analysis, ADMET prediction, clinical translation, and biomarker strategy. Analogous to awesome-drug-discovery and scientific-agent-skills.
+> *Where the prompts live, thrive, and reach the world.*
+
+Curated AI prompts spanning the full drug discovery pipeline —
+from target identification to IND-enabling studies.
+
+## Analogous Resources Ingested
+
+| Repository | Focus | Reference |
+|---|---|---|
+| [awesome-drug-discovery](https://github.com/yboulaamane/awesome-drug-discovery) | Computational drug discovery | yboulaamane |
+| [Awesome_BigData_AI_DrugDiscovery](https://github.com/Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery) | Big data & AI in pharma | Bin-Chen-Lab |
+| [Scientific-LLM-Survey](https://github.com/HICAI-ZJU/Scientific-LLM-Survey) | Scientific LLMs | HICAI-ZJU |
+| [resources_2025](https://github.com/PatWalters/resources_2025) | ML in drug discovery | PatWalters |
+| [scientific-agent-skills](https://github.com/K-Dense-AI/scientific-agent-skills) | Drug discovery agent skills | K-Dense-AI |
+
+## Pipeline Coverage
+
+```
+target-identification/ → hit-discovery/ → lead-optimisation/ → admet/ → biomarkers/ → clinical/
+```
@@ -0,0 +1,70 @@
+---
+title: "ADMET Liability Flag Interpretation"
+domain: drug-discovery
+persona: "Medicinal Chemist"
+persona_background: >
+  Senior medicinal chemist with 15+ years in pharma, specialising in SAR, lead optimisation, and ADMET.
+persona_style: "SAR-focused, uses IUPAC nomenclature, cite IC50/Ki values"
+models: [gpt-4, claude-3-5]
+keywords: [ADMET, toxicity, hERG, metabolic-stability, BBB, CYP]
+task: "Interpret ADMET prediction results and prioritise liabilities for medicinal chemistry intervention."
+validated: true
+version: 1.0.0
+author: promptadmin
+source_repositories:
+  - https://github.com/PatWalters/resources_2025
+  - https://github.com/yboulaamane/awesome-drug-discovery
+---
+
+# ADMET Liability Flag Interpretation
+
+## Persona
+
+> You are a **Medicinal Chemist**. Senior medicinal chemist with 15+ years in pharma, specialising in SAR, lead optimisation, and ADMET.
+> Your communication style: SAR-focused, uses IUPAC nomenclature, cite IC50/Ki values
+
+## Task
+
+Interpret ADMET prediction results and prioritise liabilities for medicinal chemistry intervention.
+
+## Prompt
+
+```
+You are a DMPK/toxicology expert reviewing ADMET predictions.
+
+Compound: {compound_id}
+SMILES: {smiles}
+
+ADMET predictions:
+- Solubility (µg/mL): {solubility}
+- Permeability (Caco-2, nm/s): {permeability}
+- Metabolic stability (HLM t½, min): {hlm_stability}
+- CYP inhibition: {cyp_inhibition}
+- hERG inhibition (IC50, µM): {herg_ic50}
+- Predicted Vd (L/kg): {vd}
+- BBB penetration: {bbb}
+- AMES mutagenicity: {ames}
+- Acute toxicity (LD50): {ld50}
+
+Provide:
+1. Red flags requiring immediate attention (deal-breakers)
+2. Yellow flags requiring monitoring
+3. Structural alerts (PAINS, reactive groups, toxic pharmacophores)
+4. Prioritised modifications to improve ADMET profile
+5. Go/No-go recommendation with confidence level
+```
+
+## Notes
+
+Cross-reference with ADMETlab 2.0, SwissADME, and ProTox-II. hERG IC50 < 1 µM is a hard stop in most pharma companies. Reference: PatWalters/resources_2025 for ML ADMET benchmarks.
+
+## Compatibility
+
+| Model | Tested | Notes |
+|-------|--------|-------|
+| gpt-4 | ✅ | |
+| claude-3-5 | ✅ | |
+
+## Keywords
+
+`ADMET` `toxicity` `hERG` `metabolic-stability` `BBB` `CYP`
@@ -0,0 +1,67 @@
+---
+title: "Biomarker Strategy Design"
+domain: drug-discovery
+persona: "Clinical Scientist"
+persona_background: >
+  Clinical scientist with expertise in Phase I-III trial design, GCP, and FDA/EMA regulatory submissions.
+persona_style: "regulatory-compliant, ICH-aligned, precise medical language"
+models: [gpt-4, claude-3-5]
+keywords: [biomarker, companion-diagnostic, patient-stratification, precision-medicine]
+task: "Design a biomarker strategy for a Phase II clinical trial."
+validated: false
+version: 1.0.0
+author: promptadmin
+source_repositories:
+  - https://github.com/FreedomIntelligence/Awesome-Specialized-Medical-LLMs
+  - https://github.com/K-Dense-AI/scientific-agent-skills
+---
+
+# Biomarker Strategy Design
+
+## Persona
+
+> You are a **Clinical Scientist**. Clinical scientist with expertise in Phase I-III trial design, GCP, and FDA/EMA regulatory submissions.
+> Your communication style: regulatory-compliant, ICH-aligned, precise medical language
+
+## Task
+
+Design a biomarker strategy for a Phase II clinical trial.
+
+## Prompt
+
+```
+You are a translational medicine expert designing biomarker strategies.
+
+Trial context:
+- Drug mechanism: {mechanism}
+- Indication: {indication}
+- Target patient population: {population}
+- Primary endpoint: {primary_endpoint}
+- Known mechanism-related biomarkers: {known_biomarkers}
+- Available sample types: {sample_types}
+
+Design a biomarker strategy including:
+1. **Predictive biomarkers** — patient selection/stratification
+2. **Pharmacodynamic biomarkers** — target engagement proof
+3. **Efficacy biomarkers** — early efficacy signal
+4. **Safety biomarkers** — early toxicity monitoring
+5. **Sampling schedule** — timepoints and sample volumes
+6. **Analytical platforms** — recommended assays (ELISA, NGS, IHC, etc.)
+7. **Statistical considerations** — multiplicity, sample size impact
+8. **Regulatory context** — CDx requirements if applicable
+```
+
+## Notes
+
+Aligns with FDA Biomarker Qualification Programme framework. Reference: FreedomIntelligence/Awesome-Specialized-Medical-LLMs for oncology biomarkers.
+
+## Compatibility
+
+| Model | Tested | Notes |
+|-------|--------|-------|
+| gpt-4 | ⬜ | |
+| claude-3-5 | ⬜ | |
+
+## Keywords
+
+`biomarker` `companion-diagnostic` `patient-stratification` `precision-medicine`
@@ -0,0 +1,68 @@
+---
+title: "IND Application Section Drafter"
+domain: drug-discovery
+persona: "Regulatory Affairs Specialist"
+persona_background: >
+  Global regulatory affairs director with 20 years submitting INDs, NDAs, and MAAs to FDA and EMA.
+persona_style: "formal, citation-heavy, references specific guidance documents"
+models: [gpt-4, claude-3-5]
+keywords: [IND, FDA, preclinical, regulatory, CMC, pharmacology]
+task: "Draft a specific section of an Investigational New Drug (IND) application."
+validated: false
+version: 1.0.0
+author: promptadmin
+source_repositories:
+  - https://github.com/AgenticHealthAI/Awesome-AI-Agents-for-Healthcare
+  - https://github.com/Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery
+---
+
+# IND Application Section Drafter
+
+## Persona
+
+> You are a **Regulatory Affairs Specialist**. Global regulatory affairs director with 20 years submitting INDs, NDAs, and MAAs to FDA and EMA.
+> Your communication style: formal, citation-heavy, references specific guidance documents
+
+## Task
+
+Draft a specific section of an Investigational New Drug (IND) application.
+
+## Prompt
+
+```
+You are a regulatory affairs expert with 20 years of FDA IND submission experience.
+
+Draft the following IND section:
+- Section: {ind_section}
+  (e.g., Section 8: Pharmacology and Toxicology; Section 7: Chemistry, Manufacturing, Controls)
+- Drug name (INN): {drug_name}
+- Drug class: {drug_class}
+- Indication: {indication}
+- Nonclinical data summary: {nonclinical_summary}
+- Proposed Phase I design: {phase1_design}
+
+Write the section following:
+1. FDA IND format requirements (21 CFR Part 312)
+2. ICH M4 (CTD) structure where applicable
+3. Appropriate hedging language ("data suggest", "consistent with")
+4. Cross-references to supporting studies
+5. Tables/listings as appropriate
+
+Target length: {target_length} words
+Regulatory tone: formal, objective, evidence-based
+```
+
+## Notes
+
+Always have a qualified regulatory affairs professional review before submission. Reference FDA guidance: 'Content and Format of INDs for Phase 1 Studies'. Reference: AgenticHealthAI/Awesome-AI-Agents-for-Healthcare.
+
+## Compatibility
+
+| Model | Tested | Notes |
+|-------|--------|-------|
+| gpt-4 | ⬜ | |
+| claude-3-5 | ⬜ | |
+
+## Keywords
+
+`IND` `FDA` `preclinical` `regulatory` `CMC` `pharmacology`
@@ -0,0 +1,67 @@
+---
+title: "Molecular Docking Results Interpreter"
+domain: drug-discovery
+persona: "Computational Chemist"
+persona_background: >
+  Computational chemist expert in molecular docking, QSAR modelling, and virtual screening.
+persona_style: "quantitative, references docking scores and force fields"
+models: [gpt-4, claude-3-5]
+keywords: [molecular-docking, virtual-screening, binding-pose, SMILES, AutoDock]
+task: "Interpret molecular docking results and prioritise compounds for experimental follow-up."
+validated: true
+version: 1.0.0
+author: promptadmin
+source_repositories:
+  - https://github.com/K-Dense-AI/scientific-agent-skills
+  - https://github.com/PatWalters/resources_2025
+---
+
+# Molecular Docking Results Interpreter
+
+## Persona
+
+> You are a **Computational Chemist**. Computational chemist expert in molecular docking, QSAR modelling, and virtual screening.
+> Your communication style: quantitative, references docking scores and force fields
+
+## Task
+
+Interpret molecular docking results and prioritise compounds for experimental follow-up.
+
+## Prompt
+
+```
+You are a computational chemist expert in structure-based drug design.
+
+Given molecular docking results:
+- Target protein: {target} (PDB: {pdb_id})
+- Binding site: {binding_site}
+- Docking software: {software} (version: {version})
+- Top hits:
+{hits_table}
+(Format: Compound_ID | SMILES | Docking_Score | Key_Interactions)
+
+For each compound provide:
+1. Binding pose quality assessment
+2. Key interactions (H-bonds, hydrophobic, pi-stacking, salt bridges)
+3. Comparison to known co-crystal ligands (if applicable)
+4. Synthetic accessibility estimate (1=easy, 5=very hard)
+5. ADMET flags (obvious liabilities from structure)
+6. Priority rank for experimental testing
+
+Prioritise top 3 for HTS follow-up with justification.
+```
+
+## Notes
+
+Compatible with AutoDock Vina, Glide, and GOLD output formats. Cross-reference with ChEMBL and ADMET-AI for filtering. Reference: scientific-agent-skills (K-Dense-AI).
+
+## Compatibility
+
+| Model | Tested | Notes |
+|-------|--------|-------|
+| gpt-4 | ✅ | |
+| claude-3-5 | ✅ | |
+
+## Keywords
+
+`molecular-docking` `virtual-screening` `binding-pose` `SMILES` `AutoDock`
@@ -0,0 +1,69 @@
+---
+title: "SAR Analysis and Bioisostere Suggestion"
+domain: drug-discovery
+persona: "Medicinal Chemist"
+persona_background: >
+  Senior medicinal chemist with 15+ years in pharma, specialising in SAR, lead optimisation, and ADMET.
+persona_style: "SAR-focused, uses IUPAC nomenclature, cite IC50/Ki values"
+models: [gpt-4, claude-3-5]
+keywords: [SAR, lead-optimisation, bioisostere, QSAR, potency, selectivity]
+task: "Analyse structure-activity relationships and propose bioisosteric modifications."
+validated: true
+version: 1.0.0
+author: promptadmin
+source_repositories:
+  - https://github.com/yboulaamane/awesome-drug-discovery
+  - https://github.com/HICAI-ZJU/Scientific-LLM-Survey
+---
+
+# SAR Analysis and Bioisostere Suggestion
+
+## Persona
+
+> You are a **Medicinal Chemist**. Senior medicinal chemist with 15+ years in pharma, specialising in SAR, lead optimisation, and ADMET.
+> Your communication style: SAR-focused, uses IUPAC nomenclature, cite IC50/Ki values
+
+## Task
+
+Analyse structure-activity relationships and propose bioisosteric modifications.
+
+## Prompt
+
+```
+You are a senior medicinal chemist with 15+ years in lead optimisation.
+
+Given SAR data:
+- Lead scaffold: {scaffold_smiles}
+- Biological target: {target} (assay: {assay_type})
+- SAR table:
+{sar_table}
+(Format: R-group | IC50/Ki | Selectivity | cLogP | MW)
+
+- Current liabilities: {liabilities}
+- Optimisation goal: {goal}
+
+Provide:
+1. SAR analysis — which substitution positions are most impactful?
+2. Key pharmacophoric features to maintain
+3. 5 bioisosteric modifications targeting {liability} with SMILES
+4. Predicted effect on potency/selectivity for each suggestion
+5. Synthetic feasibility assessment
+6. Next analogue priority list (top 3 to synthesise)
+
+Reference relevant patents or literature if applicable.
+```
+
+## Notes
+
+Use BoBER (Bioisosteric Replacements) database as reference. For SMILES processing, feed output to RDKit for substructure validation. Reference: awesome-drug-discovery (yboulaamane).
+
+## Compatibility
+
+| Model | Tested | Notes |
+|-------|--------|-------|
+| gpt-4 | ✅ | |
+| claude-3-5 | ✅ | |
+
+## Keywords
+
+`SAR` `lead-optimisation` `bioisostere` `QSAR` `potency` `selectivity`
@@ -0,0 +1,68 @@
+---
+title: "Drug Target Validation from Literature"
+domain: drug-discovery
+persona: "Medicinal Chemist"
+persona_background: >
+  Senior medicinal chemist with 15+ years in pharma, specialising in SAR, lead optimisation, and ADMET.
+persona_style: "SAR-focused, uses IUPAC nomenclature, cite IC50/Ki values"
+models: [gpt-4, claude-3-5]
+keywords: [target-identification, druggability, disease-association, genetic-evidence]
+task: "Evaluate and score a proposed drug target based on genetic, functional, and structural evidence."
+validated: true
+version: 1.0.0
+author: promptadmin
+source_repositories:
+  - https://github.com/yboulaamane/awesome-drug-discovery
+  - https://github.com/Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery
+---
+
+# Drug Target Validation from Literature
+
+## Persona
+
+> You are a **Medicinal Chemist**. Senior medicinal chemist with 15+ years in pharma, specialising in SAR, lead optimisation, and ADMET.
+> Your communication style: SAR-focused, uses IUPAC nomenclature, cite IC50/Ki values
+
+## Task
+
+Evaluate and score a proposed drug target based on genetic, functional, and structural evidence.
+
+## Prompt
+
+```
+You are a senior medicinal chemist and target biology expert.
+
+Evaluate the following drug target:
+- Target protein: {target_name} (Gene: {gene_symbol})
+- Disease indication: {indication}
+- Genetic evidence: {genetic_evidence}
+- Expression data: {expression_data}
+- Known tool compounds: {tool_compounds}
+- Structural data available: {structural_data}
+- Existing modality attempts: {prior_modalities}
+
+Score target on these axes (1-5 scale with justification):
+1. **Genetic evidence** (GWAS, Mendelian genetics, human LOF)
+2. **Biological rationale** (pathway relevance, disease mechanism)
+3. **Druggability** (binding pocket, physicochemical tractability)
+4. **Safety profile** (selectivity, essential gene considerations)
+5. **Competitive landscape** (freedom to operate, crowded space?)
+
+Overall recommendation: Pursue / De-prioritise / Monitor
+Confidence: High / Medium / Low
+```
+
+## Notes
+
+Uses Open Targets scoring framework. Genetic evidence is the strongest validation signal per ChatDrug (LLM-based drug discovery pipeline). Reference: awesome-drug-discovery (yboulaamane).
+
+## Compatibility
+
+| Model | Tested | Notes |
+|-------|--------|-------|
+| gpt-4 | ✅ | |
+| claude-3-5 | ✅ | |
+
+## Keywords
+
+`target-identification` `druggability` `disease-association` `genetic-evidence`
@@ -0,0 +1,70 @@
+---
+title: "Tutorials"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery/blob/d1364e08/AI.md
+upstream_sha: d1364e08
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+
+# Tutorials
+(***) [Machine Learning course at Coursera](coursera.org/learn/machine-learning) by Andrew Ng.  
+A required course for any people who are interested in machine learning.
+
+(***) [Blog posts by Adam Geitgey](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471), his code examples (tensorflow + jupyter) are amazing ([GitHub link](https://github.com/aymericdamien/TensorFlow-Examples)).  
+Step by step introduction to machine learning using tensorflow.
+
+(*) [Track deep learning related papers on Arxiv](http://www.arxiv-sanity.com/)
+
+(**) [nice blog posts from a stanford graduate Andrej Karpathy](http://karpathy.github.io/).  
+A few excellent posts to introduce deep learning from a software engineer perspective.
+
+(*) [learn important features from deep learning models](https://arxiv.org/abs/1605.01713)
+
+(**) [Berkeley CS294-112 Deep Reinforcement Learning Sp17](https://www.youtube.com/playlist?list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX)
+
+(*) [Deep Learning 2016: The Year in Review](http://www.deeplearningweekly.com/blog/deep-learning-2016-the-year-in-review)
+
+(*) [stanford deep learning in genomics and biomedicine](https://canvas.stanford.edu/courses/51037/files/folder/LectureMaterial)
+
+(*) [google Udacity deep learning course](https://classroom.udacity.com/courses/ud730)
+
+# Code
+(*) [Keras Documentation](https://keras.io/)
+
+(*) [deep learning for sequence data DragoNN](http://kundajelab.github.io/dragonn/)
+
+(*) [deep learning for chemical data: deepchem](http://deepchem.io/)
+
+(*) [mofan python (chinese)](https://morvanzhou.github.io).  
+Only provide a Chinese version, but a nice intro with code available in github.
+
+(*) add image and text in one model ([here](http://cbonnett.github.io/Insight.html))
+
+# Hot topics
+## Generative models
+(*) [GAN code ](https://github.com/Newmu/dcgan_code)
+
+(*) [Open AI generative model post](https://blog.openai.com/generative-models/#contributions)
+
+(*) [introduction-generative-adversarial-networks-code-tensorflow with github code](http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/)
+
+(**)[tips and tricks training GAN](https://github.com/soumith/ganhacks).  
+If you have trouble debugging GAN, read this.
+
+(***) [CycleGAN: Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.](https://github.com/junyanz/CycleGAN). 
+Very inspiring application of GAN in domain knowledge translation.
+
+(***) [Deep learning with cats](https://github.com/AlexiaJM/Deep-learning-with-cats).  
+Using different GAN methods to generate cats.
+
+
+# Applications
+(*) install tensorflow
+https://www.tensorflow.org/install/install_mac#ValidateYourInstallation
+
@@ -0,0 +1,41 @@
+---
+title: "Disease models"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery/blob/d1364e08/BigData.md
+upstream_sha: d1364e08
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+Refer to . 
+
+[Leveraging big data to transform target selection and drug discovery](https://www.ncbi.nlm.nih.gov/pubmed/26659699).  
+[Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma](https://www.nature.com/articles/s41575-019-0240-9).  
+
+Data Types include Genomics (G), Epigenomics (E), Transcriptomics (T), Proteomics (P), Metabolomics (M), Phenomics (P), Imaging (I) .   
+Mainly list the big data repositories
+
+# Disease models
+## Clinical models 
+
+## Preclinical models
+
+# Perturbagen 
+
+## OMICS
+(***) [A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations](https://www.biorxiv.org/content/early/2017/09/07/185918).  
+profiled 90 drugs (in triplicate) in six cell lines using two different proteomic assays
+
+(*) [Orthotopic patient-derived xenografts of paediatric solid tumours](https://www.nature.com/nature/journal/v549/n7670/full/nature23647.html).  
+sensitivity data of 150 drugs across >20 pediatric cell lines
+
+(*) [NCI ALMANAC](https://dtp.cancer.gov/ncialmanac/initializePage.do)
+5,000 pairs of FDA-approved drugs that were tested against the NCI-60
+
+# Single cells
+(***) Liver cancer single cells ([Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing](http://www.cell.com/cell/fulltext/S0092-8674(17)30596-2)).  
+5,063 single T cells isolated from peripheral blood, tumor, and adjacent normal tissues from six hepatocellular carcinoma patients.  
@@ -0,0 +1,33 @@
+---
+title: "Outstanding discoveries"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery/blob/d1364e08/DrugDiscovery.md
+upstream_sha: d1364e08
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Outstanding discoveries
+
+# Successfully Stories
+(*) The history of the [development of analogs of thalidomide](https://en.wikipedia.org/wiki/Development_of_analogs_of_thalidomide). Thalidomide is one of the most successful repurposed drug. 
+
+# Business thoughts
+(*) [Escaping Big Pharma’s Pricing With Patent-Free Drugs](https://www.nytimes.com/2017/07/18/opinion/escaping-big-pharmas-pricing-with-patent-free-drugs.html).. 
+
+(*) [Cost of Developing a Single Cancer Drug: time 7.3 years, cost $0.6 billion, revenue 1.7 billion](https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2653012?utm_source=twitter&utm_campaign=content-shareicons&utm_content=article_engagement&utm_medium=social&utm_term=091117#.WbdeR9XhT0M.twitter)
+
+(***) [Axovant's story](https://www.forbes.com/sites/matthewherper/2017/08/09/biotech-wunderkind-raises-1-1-billion-to-fund-pharma-startups/#202ba88c3308).  
+lots of stories on Axovant founded by Vivek Ramaswamy.
+
+
+# Blogs
+(**) [Robert M. Plenge](https://www.plengegen.com/robert-plenge/), Celgene VP, Harvard faculty. Focus on genetics-based drug discovery
+
+(***) [In the Pipeline](http://blogs.sciencemag.org/pipeline/).  
+A must-read blog for anyone interesting in drug discovery.
+
@@ -0,0 +1,308 @@
+---
+title: "Introduction to Bioinformatics/Cheminformatics"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery/blob/d1364e08/README.md
+upstream_sha: d1364e08
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Introduction to Bioinformatics/Cheminformatics
+(***) [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) by Robert Tibshirani.  
+If you have a little statistical background, read this book first.
+
+(***) [Machine Learning course at Coursera](https://www.coursera.org/learn/machine-learning) by Andrew Ng.  
+A required course for any people who are interested in machine learning.
+
+(***) [R & Bioconductor Manual](http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual/).  
+Ensure you have run the code before taking any bioinformatics project.
+
+(***) [STATQUEST] (https://statquest.org/video-index/).  
+Video collection for statistics, machine learning and bioinformatics.  
+
+(***) [HT Sequence Analysis with R and Bioconductor](http://manuals.bioinformatics.ucr.edu/home/ht-seq).  
+Ensure you have run the code before taking any NGS project.
+
+(***) [ChemmineR: Cheminformatics Toolkit for R](http://www.bioconductor.org/packages/devel/bioc/vignettes/ChemmineR/inst/doc/ChemmineR.html).  
+Suggest to run the code before taking any cheminformatics project.
+
+(**) [Step by Step to practice deep learning](http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html).   
+PyTorch tutorial for deep learning.
+
+(***) [Introduction to Bioinformatics and Computational Biology](https://liulab-dfci.github.io/bioinfo-combio/).   
+Very comprenhensive video tutorials in computational biology by Shirley Liu.
+
+(***) [HarvardX Biomedical Data Science Open Online Training](http://rafalab.github.io/pages/harvardx.html).   
+comprehensive tutorial on data science with code from rafalab.
+
+(***) [Statistics for biologists from StatQuest](https://statquest.org/video-index/).   
+outstanding video tutorials by Josh Starmer.
+
+(**) [Data Science Cheat Sheet](https://github.com/Bin-Chen-Lab/BigData_AI_DrugDiscovery/blob/master/data_science_cheatsheet.pdf).
+A quick check list of basics in data science (credit to Maverick Lin)
+
+(***) Single cell RNA-Seq analysis [osca](https://osca.bioconductor.org/introduction.html), [seurat](https://satijalab.org/seurat/vignettes.html). 
+outstanding framework for scRNA-Seq analysis.
+
+# Fundamental papers
+**These papers I read at least 10 times, including supplementary materials! All of them are three stars!**
+
+## Field review
+(***) [Hallmarks of Cancer: The Next Generation](http://www.cell.com/abstract/S0092-8674%2811%2900127-9), by Robert A. Weinberg.  
+Fundamental to understand cancer. 
+
+(***) [Tumor Metastasis: Molecular Insights and Evolving Paradigms](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261217/), by Robert A. Weinberg.  
+Fundamental to understand cancer metastasis.
+
+(***) [Cancer genome landscapes](http://science.sciencemag.org/content/339/6127/1546.long), by Bert Vogelstein.   
+Fundamental to understand cancer genomics.
+
+(***) [Cancer transcriptome profiling at the juncture of clinical translation](https://www.nature.com/articles/nrg.2017.96), by Arul M. Chinnaiyan.   
+review on cancer transcriptomics.
+
+(***) [Ewing sarcoma: historical perspectives, current state-of-the-art, and opportunities for targeted therapy in the future.](https://www.ncbi.nlm.nih.gov/pubmed/18525337).  
+A typical review on the therapeutic discovery of one cancer.
+
+(***) [Opportunities and challenges in phenotypic drug discovery: an industry perspective](https://www.nature.com/nrd/journal/v16/n8/abs/nrd.2017.111.html).  
+Our drug discovery approach is one type of phenotypic screening.
+
+(***) [Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002375), by Purvesh Khatri.  
+A very nice summary of method development in pathway analysis.
+
+(***) [Deep learning](https://www.nature.com/nature/journal/v521/n7553/full/nature14539.html) by Yann LeCun,	Yoshua Bengio & Geoffrey Hinton.  
+Deep learning review.
+
+(***) [High-performance medicine: the convergence of human and artificial intelligence](https://www.nature.com/articles/s41591-018-0300-7) by Eric Topol.
+Current progress and challenges in applying DL into biomedical research.
+
+## Statistical method development
+(***) [Significance analysis of microarrays applied to the ionizing radiation response](http://www.pnas.org/content/98/9/5116.full), by Robert Tibshirani.  
+Development of SAM, a popular method to perform differential expression analysis using microarray data.  
+
+(***) [limma: Linear Models for Microarray Data](https://link.springer.com/chapter/10.1007/0-387-29362-0_23).  
+Development of LIMMA, another popular method to perform differential expression analysis using microarray data.
+
+(***) [Differential expression analysis for sequence count data](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-10-r106), by Simon Anders.  
+Development of DEseq, a popular method to perform differential expression analysis using RNA-SEQ data.  
+
+(***) [Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles](http://www.pnas.org/content/102/43/15545.long), by Jill P. Mesirov.  
+Development of GSEA, the most popular gene set enrichment analysis method and the fundamental to understand our drug discovery method.
+
+(***) [Adjusting batch effects in microarray expression data using Empirical Bayes methods](https://academic.oup.com/biostatistics/article/8/1/118/252073/Adjusting-batch-effects-in-microarray-expression).  
+Development of Combat, a method to correct batch effects.
+
+(***) [Emergence of Scaling in Random Networks](http://science.sciencemag.org/content/286/5439/509.full) by Albert-László Barabási.  
+Discovery of scale-free networks.
+
+(***) [Pathsim: Meta path-based top-k similarity search in heterogeneous information networks](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.220.2455) by Jiawei Han.  
+A typical machine learning approach to mining heterogeneous networks.
+
+(***) [MuSiC: Identifying mutational significance in cancer genomes](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3409272/), by Li Ding.  
+Development of MuSic, a popular method to identify mutations.
+
+## Informatics method development and application
+(***) [The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.](http://science.sciencemag.org/content/313/5795/1929.long) by Justin Lamb.  
+The first paper to describe our drug discovery approach.
+
+(***) [Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data](http://stm.sciencemag.org/content/3/96/96ra77) from Atul's lab.  
+The basic of our drug discovery work, and a great demonstration of writing a computational paper (from method development to experimental validation).
+
+(***) [Relating protein pharmacology by ligand chemistry](http://www.nature.com/nbt/journal/v25/n2/full/nbt1284.html) by Michael J Keiser and Brian K Shoichet.  
+The development of SEA, a method to predict drug-target interactions, and another great demonstration of writing a computational paper.
+
+(***) [Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3658274/) by Peer Bork.  
+start with data analysis and end with a few biological experiments.
+
+(***) [Cross-Species Regulatory Network Analysis Identifies a Synergistic Interaction between FOXM1 and CENPF that Drives Prostate Cancer Malignancy](http://www.cell.com/cancer-cell/fulltext/S1535-6108(14)00125-1) by Andrea Califano.  
+start with data analysis and end with a few biological experiments.
+
+(***) [Elucidating compound mechanism of action by network perturbation analysis](http://www.sciencedirect.com/science/article/pii/S0092867415006996) by Andrea Califano.  
+start with data analysis and end with a few biological experiments.
+
+(***) [Discovery of drug mode of action and drug repositioning from transcriptional responses](http://www.pnas.org/content/107/33/14621.long).  
+start with data analysis and end with a few biological experiments.
+
+(***) [Imagenet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).  
+Development of convolutional neural networks (CNN), the popular deep learning method.
+
+## Computational analysis 
+(***) [Drug-target network](https://www.nature.com/nbt/journal/v25/n10/full/nbt1338.html) by Barabási.  
+Network analysis of drug-target interactions.
+
+(***) [Comprehensive molecular portraits of human breast tumours](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465532/) from TCGA.  
+A typical genomic analysis paper from TCGA.
+
+(***) [Mutational landscape and significance across 12 major cancer types.](http://dx.doi.org/10.1038/nature12634), by Li Ding.  
+A phenomenal paper on pan-cancer genomic analysis.
+
+(***) [Comprehensive Characterization of Molecular Differences in Cancer between Male and Female Patients](http://www.cell.com/cancer-cell/fulltext/S1535-6108(16)30111-8), by Han Liang.  
+A phenomenal paper on pan-cancer genomic analysis.
+
+(***) [Genetics of rheumatoid arthritis contributes to biology and drug discovery.](https://www.nature.com/nature/journal/v506/n7488/full/nature12873.html) by Robert M. Plenge.  
+great work using genetics for drug discovery.
+
+(***) [The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity](https://www.nature.com/nature/journal/v483/n7391/full/nature11003.html).  
+phenomenal work using cell line data to discover biomarkers.
+
+(***) [A comprehensive time-course–based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set](http://stm.sciencemag.org/content/7/287/287ra71.short) by Purvesh Khatri.  
+Phenomenal work using public microarray data to discover biomarkers.
+
+(***) [Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases](http://pubs.acs.org/doi/10.1021/ci060003g) by Jeremy Jenkins.  
+A typical machine learning paper in cheminformatics.
+
+(***) [Do structurally similar molecules have similar biological activity](https://dx.doi.org/10.1021/jm020155c).  
+A typical data analysis paper in cheminformatics.
+
+## Deep-learning based drug discovery
+(***) [Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells](https://pubmed.ncbi.nlm.nih.gov/33096023/) by Ideker.  
+Develop a model to predict drug activity based on a huge pharmacogenomics dataset, propose novel ways to model cells based on Gene Ontology,  and experimentally validate some hits.
+
+(***) [A Deep Learning Approach to Antibiotic Discovery](https://pubmed.ncbi.nlm.nih.gov/32084340/) by Barzilay and Collins.  
+Develop a model to predict antibiotic activity based on chemical structure, screen millions of compounds and extensively validate one drug candidate.
+
+(***) [Deep reinforcement learning for de novo drug design](http://advances.sciencemag.org/content/4/7/eaap7885) by Tropsha.  
+Develop a model to generate targeted chemical libraries of novel compounds optimized for either a single desired property or multiple properties.
+
+(***) [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://arxiv.org/abs/1509.09292) by Ryan Adams.  
+Designed DL based fingerprints inspired by ECFP
+
+(***) [Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules](https://pubs.acs.org/doi/full/10.1021/acscentsci.7b00572). 
+Used Variational autoencoder to encode SMILES and optimize compounds from the latent space.
+
+## Shape our future
+(***) [Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma](http://science.sciencemag.org/content/344/6190/1396).  
+Application of single cell in a cancer study.
+
+(***) [Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia](https://www.nature.com/nm/journal/v23/n6/full/nm.4336.html).  
+Application of single cell analysis toward personalized cancer therapy.
+
+(***) [Brown Adipogenic Reprogramming Induced by a Small Molecule](http://www.sciencedirect.com/science/article/pii/S2211124716317697) by Sheng Ding.  
+Using small molecules to control cell development.
+
+(***) [Correlating chemical sensitivity and basal gene expression reveals mechanism of action.](https://www.nature.com/nchembio/journal/v12/n2/full/nchembio.1986.html) from Stuart Schreiber.  
+Usage of pharmacogenomics data to understand drug mechanisms.
+
+(***) [A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles](https://www.biorxiv.org/content/early/2017/05/10/136168) from Todd R. Golub.  
+LINCS, the dataset we primarily used for drug discovery.
+
+(***) [Integrative clinical genomics of metastatic cancer](http://www.nature.com/nature/journal/v548/n7667/full/nature23306.html).  
+We have lots of experience working on primary cancer, now it's time to place our interest to metastatic cancer, which the majority of patients die from.
+
+(***) [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/pdf/1703.10593.pdf).  
+Using deep learning GAN to realize domain knowledge translation.
+
+# Outstanding tools and datasets for translational drug discovery
+
+**use liver cancer as an example, can be applied to other cancers, only list outstanding tools/datasets for liver cancer drug discovery**
+two review articles from the lab:  
+[Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma](https://www.nature.com/articles/s41575-019-0240-9).    
+[Leveraging big data to transform target selection and drug discovery](http://www.ncbi.nlm.nih.gov/pubmed/26659699).    
+
+## Diseases/Patients
+(***) [ClinicalTrials.gov](https://clinicaltrials.gov).  
+Search drugs used in liver cancer clinical trials.
+
+(**) [Cancer Today (Globocan): Data visualization tools that present current national estimates of cancer incidence, mortality, and prevalence](http://gco.iarc.fr/today/home).  
+Search liver cancer incidence.
+
+(*) [UK Biobank](http://www.ukbiobank.ac.uk/). [UK Biobank Engine](https://biobankengine.stanford.edu/).  
+Search public clinical/molecular data for liver cancer and search genetic variants for liver cancer via Stanford Biobank Engine.
+
+(**) [COSMIC](http://cancer.sanger.ac.uk/cosmic).  
+Search somatic mutation for liver cancer.
+
+## Target Discovery
+(***) [cBioPortal](http://www.cbioportal.org/).  
+Search molecular alterations for liver cancer from public datasets including TCGA.
+
+(***) [GTEx](http://www.gtexportal.org).  
+Search gene expression in normal liver tissues.
+
+(***) [The Human Protein Atlas](https://www.proteinatlas.org/).  
+Search protein expression and pathology for liver cancer.
+
+(***) [Cancer Cell Line Encyclopedia](https://portals.broadinstitute.org/ccle).  
+Search gene expression in liver cancer cell lines.
+
+(*) [Project Achilles](https://portals.broadinstitute.org/achilles).  
+Search essential genes in liver cancer cells.
+
+(***) [DepMap](https://depmap.org/portal/).
+create a comprehensive preclinical reference map connecting tumor features with tumor dependencies to accelerate the development of precision treatments.
+
+(***) [GEO](https://www.ncbi.nlm.nih.gov/geo/).  
+Search functional genomics data for liver cancer, requiring additional computational analysis to create a liver cancer signature.
+
+(***) [Enrichr](http://amp.pharm.mssm.edu/Enrichr/).  
+Search enriched TS/pathways/biological processes/cell types given a list of genes.
+
+(***) [STRING DB](https://string-db.org/).  
+Visualize protein-protein interactions.
+
+## Drug Discovery
+(***) [PubChem](https://pubchem.ncbi.nlm.nih.gov/).  
+Everything needed to know about a compound/drug.
+
+(**) [DrugBank](https://www.drugbank.ca/).  
+Search drug-target-indication.
+
+(**) [SEA](http://sea.bkslab.org/).  
+Predict targets of a given compound.
+
+(***) [LINCS](https://clue.io/).  
+Predict drugs given a liver cancer signature.
+
+(**) [ChemMine](http://chemmine.ucr.edu/).  
+very useful for chemical structure enrichment analysis.
+
+# NGS analysis
+(***) [RNASEQ blog](http://www.rna-seqblog.com/).  
+A great collection of RNA-SEQ analysis methods/applications.
+
+(*) [RPKM, FPKM and TPM, clearly explained](http://www.rna-seqblog.com/rpkm-fpkm-and-tpm-clearly-explained/)
+
+(*) [RNA-seq workflow: gene-level exploratory analysis and differential expression](http://www.bioconductor.org/help/workflows/rnaseqGene/)
+
+# Python packages
+(***) [anaconda](https://anaconda.org/)
+Suggest using anaconda to manage python packages.
+
+(***) [scikit: a popular python machine learning packages](http://scikit-learn.org/stable/).  
+
+(**) [rdkit](http://www.rdkit.org/docs/index.html).  
+free python library to process chemical structures.
+
+(***) [PyTorch](http://pytorch.org/).  
+Deep learning framework.
+
+# R/Bioconductor packages
+(**) [ggplot cheatsheet](http://zevross.com/blog/2014/08/04/beautiful-plotting-in-r-a-ggplot2-cheatsheet-3/). A must read to visualize data using R.  
+
+(**) [ChemmineR: Cheminformatics Toolkit for R](http://www.bioconductor.org/packages/devel/bioc/vignettes/ChemmineR/inst/doc/ChemmineR.html)
+
+(**) [biomaRt](http://bioconductor.org/packages/release/bioc/html/biomaRt.html).  
+A great package to map IDs.
+
+(***) [GEOquery](http://bioconductor.org/packages/release/bioc/html/GEOquery.html).  
+Search and download data from GEO.
+
+(**) [cgdsr](https://cran.r-project.org/web/packages/cgdsr/index.html). 
+API to access cBioportal data.
+
+(**) [pheatmap](https://cran.r-project.org/web/packages/pheatmap/index.html).  
+Visualize heatmap.
+
+(*) [Easy Way to Mix Multiple Graphs on The Same Page](http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/81-ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/).  
+
+
+
+
+
+
+
+
+
@@ -0,0 +1,584 @@
+---
+title: "Scientific Large Language Models (Sci-LLMs)"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/HICAI-ZJU/Scientific-LLM-Survey/blob/58560c16/README.md
+upstream_sha: 58560c16
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Scientific Large Language Models (Sci-LLMs)
+
+This repository collects papers on scientific large language models, particularly in the domains of biology and chemistry.
+
+> 😎 Welcome to recommend missing papers through **`Adding Issues`** or **`Pull Requests`**.
+
+## 🔔 News
+- 💥 [2025/08]Our work **[SciToolAgent](https://github.com/HICAI-ZJU/SciToolAgent)** has been accepted by **Nature Computational Science**. It is a powerful agent framework designed to integrate diverse scientific tools with LLMs.
+
+- 💥 [2025/07]Our work **[InstructBioMol](https://github.com/HICAI-ZJU/InstructBioMol)** has been accepted by **Nature Machine Intelligence**. It is a large language model that enables any-to-any alignment between biomolecules and natural language.
+
+- 💥 [2024/07] We have updated our survey paper by incorporating the latest related works. Please refer to the revised version on [arXiv](https://arxiv.org/abs/2401.14656/v2).
+
+- 💥 [2024/01] Our survey paper 'Scientific Large Language Models: A Survey on Biological & Chemical Domains' has been released on [arXiv](https://arxiv.org/abs/2401.14656/v1). 
+
+![Sci-LLMs-Scopes](figures/sci-llms-scopes.png)
+In this survey, we focus on scientific languages (i.e., textual, molecular, protein and genomic languages), as well as their combination (i.e., multimodal language).
+
+## 🌟 Contents
+
+- [Scientific Large Language Models (Sci-LLMs)](#scientific-large-language-models-sci-llms)
+  - [🔔 News](#-news)
+  - [🌟 Contents](#-contents)
+  - [📖 Textual Scientific Large Language Models (Text-Sci-LLMs)](#-textual-scientific-large-language-models-text-sci-llms)
+    - [Medical](#medical)
+    - [Biology](#biology)
+    - [Chemistry](#chemistry)
+    - [Comprehensive](#comprehensive)
+    - [Datasets and Benchmarks](#datasets-and-benchmarks)
+  - [🧪 Molecular Large Language Models (Mol-LLMs)](#-molecular-large-language-models-mol-llms)
+    - [Molecule Property Prediction](#molecule-property-prediction)
+    - [Interaction Prediction](#interaction-prediction)
+    - [Molecule Generation/Design/Edit](#molecule-generationdesignedit)
+    - [Reaction Prediction](#reaction-prediction)
+    - [Datasets and Benchmarks](#datasets-and-benchmarks-1)
+  - [🧬 Protein Large Language Models (Prot-LLMs)](#-protein-large-language-models-prot-llms)
+    - [Protein Sequence Representation](#protein-sequence-representation)
+    - [Protein Sequence Generation/Design](#protein-sequence-generationdesign)
+    - [Datasets and Benchmarks](#datasets-and-benchmarks-2)
+  - [🦠 Genomic Large Language Models (Gene-LLMs)](#-genomic-large-language-models-gene-llms)
+    - [General](#general)
+    - [Function Prediction](#function-prediction)
+    - [Variants and Evolution Prediction](#variants-and-evolution-prediction)
+    - [DNA-Protein Interaction Prediction](#dna-protein-interaction-prediction)
+    - [RNA Prediction](#rna-prediction)
+    - [Datasets and Benchmarks](#datasets-and-benchmarks-3)
+  - [Ⓜ️ Multimodal Scientific Large Language Models (MM-Sci-LLMs)](#️-multimodal-scientific-large-language-models-mm-sci-llms)
+    - [Molecule\&text](#moleculetext)
+    - [Protein\&text](#proteintext)
+    - [Protein\&molecule](#proteinmolecule)
+    - [Cell\&Text](#celltext)
+    - [Comprehensive](#comprehensive-1)
+    - [Datasets and Benchmarks](#datasets-and-benchmarks-4)
+      - [Molecule\&Text](#moleculetext-1)
+      - [Protein\&Text](#proteintext-1)
+      - [Protein\&Molecule](#proteinmolecule-1)
+      - [Cell\&Text](#celltext-1)
+      - [Comprehensive](#comprehensive-2)
+  - [👥 Contributions](#-contributions)
+    - [Citation](#citation)
+    - [Contributors](#contributors)
+    - [Contact](#contact)
+
+## 📖 Textual Scientific Large Language Models (Text-Sci-LLMs)
+
+### Medical
+- `2019.04` ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission, [arXiv](https://arxiv.org/abs/1904.05342), [Code](https://github.com/kexinhuang12345/clinicalBERT)
+- `2022.02` GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records, [arXiv](https://arxiv.org/abs/2203.03540), [Model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og)
+- `2022.12` BioMedLM, [.stanford.edu](https://crfm.stanford.edu/2022/12/15/biomedlm.html), [huggingface](https://huggingface.co/stanford-crfm/BioMedLM)
+- `2023.05` A Study of Generative Large Language Model for Medical Research and Healthcare (GatorTronGPT), [arXiv](https://arxiv.org/abs/2305.13523), [Code](https://github.com/uf-hobi-informatics-lab/GatorTronGPT)
+- `2023.11` MEDITRON-70B: Scaling Medical Pretraining for Large Language Models, [arXiv](https://arxiv.org/abs/2311.16079), [Code](https://github.com/epfLLM/meditron)
+- `2024.03` Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks (Meerkat), [arXiv](https://arxiv.org/abs/2404.00376)
+- `2023.06` ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation, [arXiv](https://arxiv.org/abs/2306.09968)
+- `2023.10` Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model, [arXiv](https://arxiv.org/abs/2310.09089), [Code](https://github.com/williamliujl/Qilin-Med/tree/master)
+- `2023.03` ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge, [arXiv](https://arxiv.org/abs/2303.14070), [Code](https://github.com/Kent0n-Li/ChatDoctor)
+- `2023.04` HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge, [arXiv](https://arxiv.org/abs/2304.06975), [Code](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese)
+- `2023.05` HuatuoGPT, towards Taming Language Model to Be a Doctor, [arXiv](https://arxiv.org/abs/2305.15075), [Code](https://github.com/FreedomIntelligence/HuatuoGPT)
+- `2023.04` Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data, [arXiv](https://arxiv.org/abs/2304.01196), [Code](https://github.com/project-baize/baize-chatbot)
+- `2023.08` Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue, [arXiv](https://arxiv.org/abs/2308.03549), [Code](https://github.com/SupritYoung/Zhongjing)
+- `2023.04` PMC-LLaMA: Towards Building Open-source Language Models for Medicine, [arXiv](https://arxiv.org/abs/2304.14454), [Code](https://github.com/chaoyi-wu/PMC-LLaMA)
+- `2023.09` CPLLM: Clinical Prediction with Large Language Models, [arXiv](https://arxiv.org/abs/2309.11295), [Code](https://github.com/nadavlab/CPLLM)
+- `2023.05` Towards Expert-Level Medical Question Answering with Large Language Models(Med-PaLM 2), [Google Research](https://sites.research.google/med-palm/), [arXiv](https://arxiv.org/abs/2305.09617)
+- `2023.05` Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding, [arXiv](https://arxiv.org/abs/2305.12031), [Code](https://github.com/bowang-lab/clinical-camel)
+- `2023.04` DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task, [arXiv](https://arxiv.org/abs/2304.01097), [Code](https://github.com/xionghonglin/DoctorGLM)
+- `2023.10` BianQue: Balancing the Questioning and Suggestion Ability of Health LLMs with Multi-turn Health Conversations Polished by ChatGPT, [arXiv](https://arxiv.org/abs/2310.15896), [Code](https://github.com/scutcyr/BianQue)
+- `2024.01` Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain, [arXiv](https://arxiv.org/abs/2404.07613)
+- `2024.02` Me LLaMA: Foundation Large Language Models for Medical Applications, [arXiv](https://arxiv.org/abs/2402.12749), [Code](https://github.com/BIDS-Xu-Lab/Me-LLaMA)
+- `2024.02` BiMediX: Bilingual Medical Mixture of Experts LLM, [arXiv](https://arxiv.org/abs/2402.13253), [Code](https://github.com/mbzuai-oryx/BiMediX), [Hugging Face](https://huggingface.co/BiMediX)
+
+
+### Biology
+- `2019.04` BioELMo: Probing Biomedical Embeddings from Language Models, [arXiv](https://arxiv.org/abs/1904.02181), [Code](https://github.com/Andy-jqa/bioelmo/)
+- `2019.05` BioBERT: a pre-trained biomedical language representation model for biomedical text mining, [arXiv](https://arxiv.org/abs/1901.08746), [Code](https://github.com/dmis-lab/biobert)
+- `2019.07` Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets, [arXiv](https://arxiv.org/abs/1906.05474), [Code](https://github.com/ncbi-nlp/bluebert?tab=readme-ov-file)
+- `2020.10` BioMegatron: Larger Biomedical Domain Language Model, [arXiv](https://arxiv.org/abs/2010.06060), [Code](https://catalog.ngc.nvidia.com/?filters=&orderBy=weightPopularDESC&query=)
+- `2020.10` Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing, [arXiv](https://arxiv.org/pdf/2007.15779.pdf), [Hugging Face](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract)
+- `2021.06` BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA, [ACL Anthology](https://aclanthology.org/2021.bionlp-1.24/), [Code](https://github.com/salrowili/BioM-Transformers)
+- `2022.03` LinkBERT: Pretraining Language Models with Document Links, [arXiv](https://arxiv.org/abs/2203.15827), [Code](https://github.com/salrowili/BioM-Transformers)
+- `2023.03` BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining, [arXiv](https://arxiv.org/abs/2210.10341), [Code](https://github.com/microsoft/BioGPT)
+- `2023.08` BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine, [arXiv](https://arxiv.org/abs/2308.09442), [Code](https://github.com/PharMolix/OpenBioMed?tab=readme-ov-file)
+- `2023.09` BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials, [arXiv](https://arxiv.org/abs/2309.08788)
+- `2024.02` BioMistral: BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains, [arXiv](https://arxiv.org/abs/2402.10373), [Code](https://github.com/BioMistral/BioMistral)
+  
+### Chemistry
+
+- `2021.06` Automated Chemical Reaction Extraction from Scientific Literature. [Journal of Chemical Information and Modeling](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.1c00284), [Code](https://github.com/jiangfeng1124/ChemRxnExtractor)
+- `2021.09` MatSciBERT: A materials domain language model for text mining and information extraction, [npj Computational Materials](https://www.nature.com/articles/s41524-022-00784-w), [Code](https://github.com/M3RG-IITD/MatSciBERT?tab=readme-ov-file)
+- `2022.09` A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing, [npj Computational Materials](https://www.nature.com/articles/s41524-023-01003-w), [Hugging Face](https://huggingface.co/pranav-s/MaterialsBERT)
+- `2024.01` ChemDFM: Dialogue Foundation Model for Chemistry, [arXiv](https://arxiv.org/abs/2402.10373), [Model](https://huggingface.co/OpenDFM/ChemDFM-13B-v1.0)
+- `2024.02` ChemLLM: A Chemical Large Language Model, [arXiv](https://arxiv.org/abs/2402.06852), [Model](https://huggingface.co/AI4Chem/ChemLLM-7B-Chat)
+- `2024.02` LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset,  [arXiv](https://arxiv.org/abs/2402.09391), [Page](https://osu-nlp-group.github.io/LLM4Chem/), [Model](https://huggingface.co/osunlp/LlaSMol-Mistral-7B), [Dataset](https://huggingface.co/datasets/osunlp/SMolInstruct)
+- `2024.02` PharmaGPT: Domain-Specific Large Language Models for Bio-Pharmaceutical and Chemistry, [arXiv](https://arxiv.org/pdf/2406.18045v2)
+
+
+### Comprehensive
+
+- `2019.09` SciBERT: A Pretrained Language Model for Scientific Text, [arXiv](https://arxiv.org/abs/1903.10676), [Code](https://github.com/allenai/scibert/)
+- `2023.05` The Diminishing Returns of Masked Language Models to Science, [arXiv](https://arxiv.org/abs/2205.11342), [Hugging Face](https://huggingface.co/globuslabs/ScholarBERT)
+- `2023.08` DARWIN Series: Domain Specific Large Language Models for Natural Science, [arXiv](https://arxiv.org/abs/2308.13565), [Code](https://github.com/MasterAI-EAM/Darwin)
+- `2024.01` SciGLM: Training Scientific Language Models with Self-Reflective Instruction Annotation and Tuning, [arXiv](https://arxiv.org/abs/2401.07950), [GitHub](https://github.com/THUDM/SciGLM)
+- `2024.03` Uni-SMART: Universal Science Multimodal Analysis and Research Transformer, [arXiv](https://arxiv.org/abs/2403.10301)
+- `2024.05` INDUS: Effective and Efficient Language Models for Scientific Applications,[arXiv](https://arxiv.org/abs/2405.10725)
+- `2024.09` SciDFM: A Large Language Model with Mixture-of-Experts for Science, [arXiv](https://arxiv.org/pdf/2409.18412), [Code](https://huggingface.co/OpenDFM/SciDFM-MoE-A5.6B-v1.0)
+
+  
+### Datasets and Benchmarks
+
+- [The MIMIC dataset](https://mimic.mit.edu/), `2016.05`. [mimic-code](https://github.com/MIT-LCP/mimic-code), Data Descriptor: MIMIC-III, a freely accessible critical care database, [Scientific Data](https://www.nature.com/articles/sdata201635.pdf)
+- [eICU-CRD](https://physionet.org/content/eicu-crd/2.0/). `2019.04`. The eICU Collaborative Research Database, a freely available multi-center database for critical care research, [Scientific Data](https://www.nature.com/articles/sdata2018178)
+- [cMedQA2](https://github.com/zhangsheng93/cMedQA2?tab=readme-ov-file), `2018.11`. Multi-Scale Attentive Interaction Networks for Chinese Medical Question Answer Selection, [IEEE Access](https://ieeexplore.ieee.org/abstract/document/8548603)
+- [MedDialog-Chinese](https://tianchi.aliyun.com/dataset/92110). MedDialog: Large-scale Medical Dialogue Datasets, [EMNLP 2020](https://aclanthology.org/2020.emnlp-main.743/)
+- [ChiMed](https://huggingface.co/datasets/williamliu/ChiMed/tree/main). `2023.10`. Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model, [arXiv](https://arxiv.org/abs/2310.09089v2)
+- [HealthCareMagic-100k](www.healthcaremagic.com), `2023.03`. ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge, [arXiv](https://arxiv.org/abs/2303.14070)
+- [MedQuAD](https://github.com/abachaa/MedQuAD), `2019.01`. A Question-Entailment Approach to Question Answering, [arXiv](https://arxiv.org/abs/1901.08079v1)
+- [MultiMedQA](https://www.nature.com/articles/s41586-023-06291-2), `2023.07`. Large language models encode clinical knowledge, [Nature](https://www.nature.com/articles/s41586-023-06291-2)
+- [Open-I](https://openi.nlm.nih.gov/), `2015.07`. Preparing a collection of radiology examinations for distribution and retrieval, [JAMIA](https://academic.oup.com/jamia/article/23/2/304/2572395?login=false)
+- [Psych8k](https://huggingface.co/datasets/EmoCareAI/Psych8k), `2024.03`. ChatCounselor: A Large Language Models for Mental Health Support, [arXiv](https://arxiv.org/abs/2309.15461)
+- [CMD. dataset](https://github.com/Toyhom/Chinese-medical-dialogue-data), `2019.09`. DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task, [arXiv](https://arxiv.org/abs/2304.01097)
+- [BianQueCorpus](https://github.com/scutcyr/BianQue), `2023.10`. BianQue: Balancing the Questioning and Suggestion Ability of Health LLMs with Multi-turn Health Conversations Polished by ChatGPT, [arXiv](https://arxiv.org/abs/2310.15896)
+- [MedQA-USMLE](https://github.com/jind11/MedQA), `2021.04`. What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams, [arXiv](https://arxiv.org/abs/2009.13081)
+- [MedMCQA](https://github.com/medmcqa/medmcqa), `2022.03`. MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering, [arXiv](https://arxiv.org/abs/2203.14371)
+- [JAMA Clinical Challenge dataset](https://github.com/HanjieChen/ChallengeClinicalQA), `2024.05`. Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions, [arXiv](https://arxiv.org/abs/2402.18060)
+- [CMtMedQA](https://huggingface.co/datasets/Suprit/CMtMedQA), `2023.08`. Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue, [arXiv](https://arxiv.org/abs/2308.03549)
+- [huatuo-26M](https://github.com/FreedomIntelligence/Huatuo-26M), `2023.05`. Huatuo-26M, a Large-scale Chinese Medical QA Dataset, [arXiv](https://arxiv.org/abs/2305.01526)
+- [MMLU](https://huggingface.co/datasets/cais/mmlu), `2020.09`. Measuring Massive Multitask Language Understanding, [arXiv](https://arxiv.org/abs/2009.03300)
+- [C-Eval](https://huggingface.co/datasets/ceval/ceval-exam), `2023.05`. C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models, [arXiv](https://arxiv.org/abs/2305.08322)
+- [AGIEval](https://huggingface.co/datasets/baber/agieval) `2023.05`. AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models, [arXiv](https://arxiv.org/abs/2304.06364)
+- [ScienceQA](https://huggingface.co/datasets/derek-thomas/ScienceQA), 2022.09. Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering, [arXiv](https://arxiv.org/abs/2209.09513)
+- [Xiezhi](https://github.com/MikeGu721/XiezhiBenchmark), `2023.06`. Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation, [arXiv](https://arxiv.org/abs/2306.05783)
+- [SciEval](https://github.com/OpenDFM/SciEval), `2023.08`. SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research, [arXiv](https://arxiv.org/abs/2308.13149)
+- [Bioinfo-Bench](https://github.com/cinnnna/bioinfo-bench), `2023.10`. A Simple Benchmark Framework for LLM Bioinformatics Skills Evaluation, [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.10.18.563023v1.abstract)
+- [BLURB](https://huggingface.co/datasets/EMBO/BLURB), `2020.07`. Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing, [arXiv](https://arxiv.org/abs/2007.15779)
+- [ARC](https://huggingface.co/datasets/allenai/ai2_arc), `2018.03`. Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge, [arXiv](https://arxiv.org/abs/1803.05457)
+- [SciQ](https://huggingface.co/datasets/sciq), `2017.07`. Crowdsourcing Multiple Choice Science Questions, [arXiv](https://arxiv.org/abs/1707.06209v1)
+
+## 🧪 Molecular Large Language Models (Mol-LLMs)
+
+### Molecule Property Prediction
+
+- `2019.09` SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction, [ACM-BCB](https://dl.acm.org/doi/10.1145/3307339.3342186), [Code](https://github.com/uta-smile/SMILES-BERT)
+- `2019.11` SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery, [arXiv](https://arxiv.org/abs/1911.04738v1), [Code](https://github.com/DSPsleeporg/smiles-transformer)
+- `2020.02` Molecule attention transformer, [arXiv](http://arxiv.org/abs/2002.08264), [Code](https://github.com/ardigen/MAT)
+- `2020.10` ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction, [arXiv](http://arxiv.org/abs/2010.09885), [Code](https://huggingface.co/seyonec/ChemBERTa-zinc-base-v1)
+- `2020.10` Self-Supervised Graph Transformer on Large-Scale Molecular Data, [arXiv](http://arxiv.org/abs/2007.02835), [Code](https://github.com/tencent-ailab/grover)
+- `2020.11` Language models in molecular discovery, [NeurIPS](https://arxiv.org/abs/2011.13230), [Code](https://github.com/BenevolentAI/MolBERT)
+- `2021.05` MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction, [Briefings in Bioinformatics](https://www.researchgate.net/publication/351363304_MG-BERT_leveraging_unsupervised_atomic_representation_learning_for_molecular_property_prediction), [Code](https://github.com/zhang-xuan1314/Molecular-graph-BERT)
+- `2021.06` Algebraic graph-assisted bidirectional transformers for molecular property prediction, [Nature Communications](https://www.nature.com/articles/s41467-021-23720-w), [Code](https://github.com/ChenDdon/AGBTcode)
+- `2021.09` Mol-BERT: An Effective Molecular Representation with BERT for Molecular Property Prediction, [Wireless Communications and Mobile Computing](https://www.hindawi.com/journals/wcmc/2021/7181815/), [Code](https://github.com/cxfjiang/MolBERT)
+- `2021.10` Relative molecule self-attention transformer, [Journal of Cheminformatics](https://link.springer.com/article/10.1186/s13321-023-00789-7), [Code](https://github.com/gmum/huggingmolecules) 
+- `2022.08` KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction, [Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining](https://dl.acm.org/doi/10.1145/3534678.3539426), [Code](https://github.com/lihan97/KPGT)
+- `2022.09` ChemBERTa-2: Towards Chemical Foundation Models, [arXiv](http://arxiv.org/abs/2209.01712), [Code](https://huggingface.co/seyonec/ChemBERTa-zinc-base-v1)
+- `2022.01` Chemformer: a pre-trained transformer for computational chemistry, [Mach. Learn.: Sci. Technol.](https://iopscience.iop.org/article/10.1088/2632-2153/ac3ffb/meta), [Code](https://github.com/MolecularAI/Chemformer)
+- `2022.10` Large-Scale Distributed Training of Transformers for Chemical Fingerprinting, [JCIM](https://doi.org/10.1021/acs.jcim.2c00715), [Code](https://github.com/GouldGroup/MFBERT)
+- `2022.11` BARTSmiles: Generative Masked Language Models for Molecular Representations, [arXiv](https://arxiv.org/abs/2211.16349), [Code](https://github.com/YerevaNN/BARTSmiles/)
+- `2022.12` Large-Scale Chemical Language Representations Capture Molecular Structure and Properties, [arXiv](http://arxiv.org/abs/2106.09553), [Code](https://github.com/IBM/molformer)
+- `2022.12` Pushing the Boundaries of Molecular Property Prediction for Drug Discovery with Multitask Learning BERT Enhanced by SMILES Enumeration, [Research](https://spj.science.org/doi/10.34133/research.0004), [Code](https://github.com/zhang-xuan1314/MTL-BERT)
+- `2023.01` MolRoPE-BERT: An enhanced molecular representation with Rotary Position Embedding for molecular property prediction, [Journal of Molecular Graphics and Modelling](https://linkinghub.elsevier.com/retrieve/pii/S1093326322002236)
+- `2023.01` Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs, [arXiv](http://arxiv.org/abs/2110.01191), [Code](https://github.com/smiles724/Molformer)
+- `2023.02` UNI-MOL: A UNIVERSAL 3D MOLECULAR REPRESENTATION LEARNING FRAMEWORK, [NeurIPS](https://chemrxiv.org/engage/chemrxiv/article-details/6402990d37e01856dc1d1581), [Code](https://github.com/dptech-corp/Uni-Mol)
+- `2023.05` SELFORMER: MOLECULAR REPRESENTATION LEARNING VIA SELFIES LANGUAGE MODELS, [arXiv](http://arxiv.org/abs/2304.04662), [Code](https://github.com/HUBioDataLab/SELFormer)
+- `2023.06` GS-Meta: Graph Sampling-based Meta-Learning for Molecular Property Prediction, [arXiv](https://arxiv.org/pdf/2306.16780), [code](https://github.com/HICAI-ZJU/GS-Meta.git)
+- `2023.07` Molecular Descriptors Property Prediction Using Transformer-Based Approach, [IJMS](https://www.mdpi.com/1422-0067/24/15/11948)
+- `2023.10` GTMGC: USING GRAPH TRANSFORMER TO PREDICT MOLECULE’S GROUND-STATE CONFORMATION, [ICLR](https://openreview.net/forum?id=F7QnIKlC1N)
+- `2023.10` iMoLD: Learning Invariant Molecular Representation in Latent Discrete Space, [arXiv](https://arxiv.org/abs/2310.14170), [code](https://github.com/HICAI-ZJU/iMoLD.git)
+
+### Interaction Prediction
+
+- `2020.12` X-MOL: large-scale pre-training for molecular understanding and diverse molecular analysis, [bioRxiv](https://www.bioRxiv.org/content/10.1101/2020.12.23.424259v2.full), [Code](https://github.com/bm2-lab/X-MOL)
+
+### Molecule Generation/Design/Edit
+
+- `2021.05` MolGPT: Molecular Generation Using a Transformer-Decoder Model, [JCIM](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.1c00600), [Code](https://github.com/devalab/molgpt)
+- `2021.07` Transmol: repurposing a language model for molecular generation, [RSC Advances](https://pubs.rsc.org/en/content/articlelanding/2021/ra/d1ra03086h), [Code](https://gitlab.com/cheml.io/public/transmol)
+- `2021.09` GENERATIVE PRE-TRAINING FROM MOLECULES, [ChemRxiv](https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6142f60742198e8c31782e9e/original/generative-pre-training-from-molecules.pdf), [Code](https://github.com/sanjaradylov/smiles-gpt)
+- `2021.12` Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention, [JCIM](https://pubs.acs.org/doi/10.1021/acs.jcim.1c01289), [Code](https://github.com/Hyunseung-Kim/molGCT)
+- `2022.10` A Pre-trained Conditional Transformer for Target-specific De Novo Molecular Generation, [arXiv](http://arxiv.org/abs/2210.08749)
+- `2023.05` iupacGPT: IUPAC-based large-scale molecular pre-trained model for property prediction and molecule generation, [ChemRxiv](https://chemrxiv.org/engage/chemrxiv/article-details/645f49f9a32ceeff2d90c9ae), [Code](https://github.com/AspirinCode/iupacGPT)
+- `2023.05` cMolGPT: A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation, [Molecules](https://www.mdpi.com/1420-3049/28/11/4430), [Code](https://github.com/VV123/cMolGPT)
+- `2023.05` Molecule generation using transformers and policy gradient reinforcement learning, [Scientific Reports](https://www.nature.com/articles/s41598-023-35648-w), [Code](https://github.com/eyalmazuz/MolGen)
+- `2023.10` DOMAIN-AGNOSTIC MOLECULAR GENERATION WITH SELF-FEEDBACK, [arXiv](https://arxiv.org/abs/2301.11259v5), [Code](https://github.com/zjunlp/MolGen)
+- `2025.06` Boosting LLM's Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning, [arXiv](https://arxiv.org/abs/2506.23056), [code](https://github.com/HICAI-ZJU/K-MSE.git)
+
+### Reaction Prediction
+
+- `2019.08` Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction, [ACS Cent. Sci.](https://doi.org/10.1021/acscentsci.9b00576), [Code](https://github.com/pschwllr/MolecularTransformer)
+- `2019.08` Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space, [Chemical Communications](http://xlink.rsc.org/?DOI=C9CC05122H)
+- `2019.09` A Transformer Model for Retrosynthesis, [ICANN](http://link.springer.com/10.1007/978-3-030-30493-5_78), [Code](https://github.com/bigchem/retrosynthesis)
+- `2019.12` Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks, [arXiv](https://arxiv.org/abs/1907.01356), [Code](https://github.com/Jh-SYSU/SCROP)
+- `2020.11` State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis, [Nature Communications](https://www.nature.com/articles/s41467-020-19266-y), [Code](https://github.com/bigchem/synthesis)
+- `2021.01` Valid, Plausible, and Diverse Retrosynthesis Using Tied Two-Way Transformers with Latent Variables, [JCIM](https://pubs.acs.org/doi/10.1021/acs.jcim.0c01074), [Code](https://github.com/ejklike/tied-twoway-transformer/)
+- `2021.01` Prediction of chemical reaction yields using deep learning, [Mach. Learn.: Sci. Technol.](https://iopscience.iop.org/article/10.1088/2632-2153/abc81d), [Code](https://rxn4chemistry.github.io/rxn_yields/)
+- `2021.03` Predicting Chemical Reaction Outcomes: A Grammar Ontology-based Transformer Framework, [AIChE Journal](https://aiche.onlinelibrary.wiley.com/doi/10.1002/aic.17190)
+- `2021.10` Molecular Graph Enhanced Transformer for Retrosynthesis Prediction, [Neurocomputing](https://www.sciencedirect.com/science/article/pii/S0925231221009413), [Code](https://github.com/papercodekl/MolecularGET)
+- `2021.10` PERMUTATION INVARIANT GRAPH-TO-SEQUENCE MODEL FOR TEMPLATE-FREE RETROSYNTHESIS AND REACTION PREDICTION, [arXiv](https://arxiv.org/abs/2110.09681), [Code](https://github.com/coleygroup/Graph2SMILES)
+- `2022.03` Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments, [Nature Communications](https://www.nature.com/articles/s41467-022-28857-w), [Code](https://github.com/knu-lcbc/RetroTRAE)
+- `2023.02` Enhancing diversity in language based models for single-step retrosynthesis, [Digital Discovery](http://xlink.rsc.org/?DOI=D2DD00110A), [Code](https://github.com/rxn4chemistry/rxn_cluster_token_prompt)
+- `2023.07` Unbiasing Retrosynthesis Language Models with Disconnection Prompts, [ACS Cent. Sci.](https://doi.org/10.1021/acscentsci.3c00372), [Code](https://github.com/rxn4chemistry/disconnection_aware_retrosynthesis)
+
+### Datasets and Benchmarks
+
+- [ZINC 15](https://ZINC15.docking.org/), `2015.10` ZINC 15 – Ligand Discovery for Everyone, [JCIM](https://pubs.acs.org/doi/full/10.1021/acs.jcim.5b00559)
+- [ZINC 20](https://zinc20.docking.org/), `2020.12` ZINC20—A Free Ultralarge-Scale Chemical Database for Ligand Discovery, [JCIM](https://pubs.acs.org/doi/full/10.1021/acs.jcim.5b00559)
+- [ZINC-250k](https://figshare.com/articles/dataset/ZINC_250K_data_sets/17122427/1), `2012.07` ZINC − A Free Database of Commercially Available Compounds for Virtual Screening, [JCIM](https://pubs.acs.org/doi/10.1021/ci049714%2B?ref=PDF)
+- [PubChem](https://pubchem.ncbi.nlm.nih.gov/), `2023.01` PubChem 2023 update, [Nucleic Acids Research](https://doi.org/10.1093/nar/gkac956)
+- [USPTO](https://developer.uspto.gov/data), [USPTO MIT](https://github.com/wengong-jin/nips17-rexgen), [USPTO-15K](https://github.com/connorcoley/ochem_predict_nn), [USPTO-full](https://github.com/dan2097/patent-reaction-extraction), `2012.10` Extraction of chemical structures and reactions from the literature, [University of Cambridge](http://www.dspace.cam.ac.uk/handle/1810/244727)
+- [PCQM4Mv2](https://ogb.stanford.edu/docs/lsc/pcqm4mv2/), `2021.10` OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs, [arXiv](http://arxiv.org/abs/2103.09430)
+- [PCQM4M-LSC](https://ogb.stanford.edu/kddcup2021/pcqm4m/), `2021.06` First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track, [arXiv](http://arxiv.org/abs/2106.08279)
+- [GEOM](https://www.aicures.mit.edu/), `2022.04` GEOM, energy-annotated molecular conformations for property prediction and molecular generation, [Nature](https://www.nature.com/articles/s41597-022-01288-4)
+- [ToyMix, LargeMix, UltraLarge](https://zenodo.org/record/8372621), `2023.10` Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets, [arXiv](https://arxiv.org/pdf/2310.04292v2.pdf)
+- [ChEMBL](https://www.ebi.ac.uk/chembl/), `2023.05` The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods, [Nucleic Acids Research](https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkad1004/7337608)
+- [DrugBank 5.0](https://go.drugbank.com/), `2017.11` DrugBank 5.0: a major update to the DrugBank database for 2018, [Nucleic Acids Research](https://academic.oup.com/nar/article/46/D1/D1074/4602867?login=false)
+- [GDB-17](https://gdb.unibe.ch/downloads/), `2012.10` Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17, [JCIM](https://pubs.acs.org/doi/10.1021/ci300415d)
+- [ExCAPE-DB](https://pubchem.ncbi.nlm.nih.gov/), `2017.03` ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics, [Journal of Cheminformatics](https://doi.org/10.1186/s13321-017-0203-5)
+- [MoleculeNet](https://github.com/deepchem/deepchem), `2017.10` MoleculeNet: a benchmark for molecular machine learning, [Chemical Science](https://pubs.rsc.org/en/content/articlelanding/2018/sc/c7sc02664a)
+- [MARCEL](https://github.com/SXKDZ/MARCEL), `2023.09` Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks, [arXiv](https://arxiv.org/abs/2310.00115v1)
+- [GuacaMol](https://benevolent.ai/guacamol), `2019.03` GuacaMol: Benchmarking Models for de Novo Molecular Design, [JCIM](https://pubs.acs.org/doi/10.1021/acs.jcim.8b00839?ref=PDF)
+- [MOSES](https://github.com/molecularsets/moses), `2020.12` Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models, [Frontiers in Pharmacology](https://www.frontiersin.org/articles/10.3389/fphar.2020.565644/full)
+- [ADMETlab 2.0](https://admet.scbdd.com/), `2021.04` ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties, [Nucleic Acids Research](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262709/)
+- [SPECTRA](https://github.com/mims-harvard/SPECTRA), `2024.02` Evaluating generalizability of artificial intelligence models for molecular datasets, [bioRxiv](https://www.biorxiv.org/content/10.1101/2024.02.25.581982v1)
+- [Molecule3D](https://github.com/divelab/MoleculeX), `2021.09` Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs, [arXiv](https://arxiv.org/abs/2110.01717)
+
+## 🧬 Protein Large Language Models (Prot-LLMs)
+
+### Protein Sequence Representation
+
+- `2020.02` Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences, [PNAS](https://www.bioRxiv.org/content/10.1101/622803v4), [Code](https://github.com/facebookresearch/esm)
+- `2021.02` MSA transformer, [PMLR](http://proceedings.mlr.press/v139/rao21a.html), [Code](https://github.com/facebookresearch/esm)
+- `2021.02` Multi-scale representation learning on proteins, [Neurips](https://proceedings.neurips.cc/paper_files/paper/2021/hash/d494020ff8ec181ef98ed97ac3f25453-Abstract.html)
+- `2021.02` Language models enable zero-shot prediction of the effects of mutations on protein function, [Neurips](https://proceedings.neurips.cc/paper_files/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html), [Code](https://github.com/facebookresearch/esm)
+- `2021.07` ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning, [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://ieeexplore.ieee.org/abstract/document/9477085/), [Code](https://github.com/agemagician/ProtTrans)
+- `2021.07` Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model, [CoRR](https://arxiv.org/abs/2110.15527)
+- `2021.09` Toward more general embeddings for protein design: Harnessing joint representations of sequence and structure, [bioRxiv](https://www.bioRxiv.org/content/10.1101/2021.09.01.458592.abstract)
+- `2022.02` ProteinBERT: a universal deep-learning model of protein sequence and function, [bioRxiv](https://academic.oup.com/bioinformatics/article-abstract/38/8/2102/6502274), [Code](https://github.com/nadavbra/protein_bert)
+- `2022.04` Lm-gvp: an extensible sequence and structure informed deep learning framework for protein property prediction, [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.02.22.529597v1), [Code](https://github.com/aws-samples/lm-gvp)
+- `2022.05` Retrieved Sequence Augmentation for Protein Representation Learning, [bioRxiv](https://www.bioRxiv.org/content/10.1101/622803v4), [Code](https://github.com/HKUNLP/RSA)
+- `2022.06` OntoProtein: Protein Pretraining With Gene Ontology Embedding, [arXiv](https://ui.adsabs.harvard.edu/abs/2022arXiv220111147Z), [Code](https://github.com/zjunlp/OntoProtein)
+- `2022.07` Language models of protein sequences at the scale of evolution enable accurate structure prediction, [bioRxiv](https://doi.org/10.1101/2022.07.20.500902), [Code](https://github.com/facebookresearch/esm)
+- `2023.02` Multi-level Protein Structure Pre-training via Prompt Learning, [ICLR](https://openreview.net/forum?id=XGagtiJ8XC), [Code](https://github.com/HICAI-ZJU/PromptProtein)
+- `2023.02` Protein Representation Learning via Knowledge Enhanced Primary Structure Modeling, [arXiv](https://ui.adsabs.harvard.edu/abs/2023arXiv230113154Z), [Code](https://github.com/RL4M/KeAP)
+- `2023.10` Deciphering the protein landscape with ProtFlash, a lightweight language model, [bioRxiv](https://doi.org/10.1016/j.xcrp.2023.101600), [Code](https://github.com/ISYSLAB-HUST/ProtFlash)
+- `2023.10` Enhancing protein language models with structure-based encoder and pre-training, [arXiv](https://arxiv.org/abs/2303.06275), [Code](https://github.com/DeepGraphLearning/ESM-GearNet)
+- `2023.10` Saprot: Protein language modeling with structure-aware vocabulary, [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.10.01.560349v2.abstract), [Code](https://github.com/westlake-repl/SaProt)
+- `2023.12` ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers, [bioRxiv](https://www.bioRxiv.org/content/early/2023/12/07/2023.12.06.570473)
+- `2024.02` Codon language embeddings provide strong signals for use in protein engineering, [Nature Machine Intelligence](https://www.nature.com/articles/s42256-024-00791-0#data-availability), [Code](https://github.com/oxpig/CaLM)
+- `2024.04` Pre-training Sequence, Structure, and Surface Features for Comprehensive Protein Representation Learning, [ICLR](https://openreview.net/forum?id=BEH4mGo7zP)
+- `2024.06` ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling, [bioRxiv](https://www.biorxiv.org/content/10.1101/2024.03.04.583284v3.abstract)
+- `2024.06` Knowledge-aware Reinforced Language Models for Protein Directed Evolution, [ICML](https://openreview.net/pdf?id=MikandLqtW)
+- `2024.06` Simulating 500 million years of evolution with a language model, [bioRxiv](https://evolutionaryscale-public.s3.us-east-2.amazonaws.com/research/esm3.pdf), [Code](https://github.com/evolutionaryscale/esm)
+
+### Protein Sequence Generation/Design
+
+- `2020.03` ProGen: Language Modeling for Protein Generation, [arXiv](https://doi.org/10.48550/arXiv.2004.03497), [Code](https://github.com/salesforce/progen)
+- `2021.01` A deep unsupervised language model for protein design, [bioRxiv](https://www.bioRxiv.org/content/early/2022/03/12/2022.03.09.483666), [Code](https://huggingface.co/nferruz/ProtGPT2)
+- `2021.01` Fold2seq: A joint sequence (1d)-fold (3d) embedding-based generative model for protein design, [PMLR](https://proceedings.mlr.press/v139/cao21a.html), [Code](https://github.com/IBM/fold2seq)
+- `2022.01` ZymCTRL: a conditional language model for the controllable generation of artificial enzymes, [NeurIPS](https://www.mlsb.io/papers_2022/ZymCTRL_a_conditional_language_model_for_the_controllable_generation_of_artificial_enzymes.pdf), [Code](https://huggingface.co/AI4PD/ZymCTRL)
+- `2022.04` Few Shot Protein Generation, [arXiv](https://arxiv.org/abs/2204.01168)
+- `2022.05` RITA: a Study on Scaling Up Generative Protein Sequence Models, [arXiv]([https://doi.org/10.48550/arXiv.2004.03497](https://ui.adsabs.harvard.edu/abs/2022arXiv220505789H))
+- `2022.12` Generative language modeling for antibody design, [arXiv](https://www.bioRxiv.org/content/10.1101/2021.12.13.472419.abstract), [Code](https://github.com/Graylab/IgLM)
+- `2023.02` Structure-informed Language Models Are Protein Designers, [bioRxiv](https://www.bioRxiv.org/content/early/2023/02/09/2023.02.03.526917)
+- `2023.02` Generative power of a protein language model trained on multiple sequence alignments, [Elife](https://elifesciences.org/articles/79854), [Code](https://doi.org/10.5281/zenodo.7684052)
+- `2023.02` Protein sequence design in a latent space via model-based reinforcement learning, [ICLR](https://openreview.net/forum?id=OhjGzRE5N6o)
+- `2023.06` Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation, [arXiv](https://ui.adsabs.harvard.edu/abs/2023arXiv230601824Z), [Code](https://github.com/Magiccircuit/MSA-Augmentor)
+- `2023.07` ProstT5: Bilingual Language Model for Protein Sequence and Structure, [bioRxiv](https://www.bioRxiv.org/content/early/2023/07/25/2023.07.23.550085), [Code](https://github.com/mheinzinger/ProstT5)
+- `2023.07` xTrimoPGLM: unified 100B-scale pre-trained transformer for deciphering the language of protein, [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.07.05.547496.abstract)
+- `2023.08` Efficient and accurate sequence generation with small-scale protein language models, [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.08.04.551626.abstract)
+- `2023.10` Generative Antibody Design for Complementary Chain Pairing Sequences through Encoder-Decoder Language Model, [NeurIPS](https://openreview.net/forum?id=QrH4bhWhwY)
+- `2023.10` ProGen2: exploring the boundaries of protein language models, [Cell](https://www.cell.com/cell-systems/pdf/S2405-4712(23)00272-7.pdf), [Code](https://github.com/salesforce/progen)
+- `2023.10` ProteinRL: Reinforcement learning with generative protein language models for property-directed sequence design, [NeurIPS](https://openreview.net/forum?id=sWCsSKqkXa)
+- `2023.11` PoET: A generative model of protein families as sequences-of-sequences, [arXiv](https://arxiv.org/abs/2306.06156)
+- `2024.03` Protein Discovery with Discrete Walk-Jump Sampling, [arxiv](https://arxiv.org/abs/2306.12360).
+
+### Datasets and Benchmarks
+
+- [UniRef100, 90, 50](https://www.uniprot.org/uniref?query=*), `2007.03` UniRef: comprehensive and non-redundant UniProt reference clusters, [Bioinformatics](https://academic.oup.com/bioinformatics/article-abstract/23/10/1282/197795)
+- [UniProtKB/Swiss-Prot](https://www.uniprot.org/uniprotkb?query=*), `2016.01` UniProtKB/Swiss-Prot, the manually annotated section of the UniProt KnowledgeBase: how to use the entry view, [Springer Plant Bioinformatics](https://link.springer.com/protocol/10.1007/978-1-4939-3167-5_2)
+- [UniProtKB/TrEMBL](https://www.uniprot.org/uniprotkb?query=*), `1999.03` EDITtoTrEMBL: a distributed approach to high-quality automated protein sequence annotation, [Bioinformatics](https://link.springer.com/protocol/10.1007/978-1-4939-3167-5_2)
+- [UniParc](https://www.uniprot.org/uniparc?query=*), `2022.11` UniProt: the Universal Protein Knowledgebase in 2023, [Bioinformatics](https://doi.org/10.1093/nar/gkac1052)
+- [Pfam](https://www.ebi.ac.uk/interpro/entry/pfam/), `1999.03` Pfam: clans, web tools and services, [Nucleic Acids Research](https://academic.oup.com/nar/article-abstract/34/suppl_1/D247/1133922)
+- [BFD](https://bfd.mmseqs.com/), `2018.06` Clustering huge protein sequence sets in linear time, [Nature Communications](https://www.nature.com/articles/s41467-018-04964-5)
+- [PDB](https://www.rcsb.org/), `2018.10` Protein Data Bank: the single global archive for 3D macromolecular structure data, [Nucleic Acids Research](https://doi.org/10.1093/nar/gky949)
+- [AlphaFoldDB](https://alphafold.ebi.ac.uk/), `2021.11` AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models, [Nucleic Acids Research](https://doi.org/10.1093/nar/gkab1061)
+- [CASP](https://predictioncenter.org/), `2021.09` Critical assessment of methods of protein structure prediction (CASP)—Round XIV, [PROTEINS](https://doi.org/10.1002/prot.26237)
+- [EC](https://www.enzyme-database.org/), `2008.09` ExplorEnz: the primary source of the IUBMB enzyme list, [Nucleic Acids Research](https://academic.oup.com/nar/article-abstract/37/suppl_1/D593/1000297)
+- [GO](https://geneontology.org/), `2000.05` Gene ontology: tool for the unification of biology, [Nature Genetics](https://www.nature.com/articles/ng0500_25)
+- [CATH](http://www.cathdb.info), `1997.08` CATH--a hierarchic classification of protein domain structures, [NIH](https://pubmed.ncbi.nlm.nih.gov/9309224/)
+- [HIPPIE](http://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/), `2012.02` HIPPIE: Integrating protein interaction networks with experiment based quality scores, [PLoS ONE](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0031826)
+- [SCOP](http://scop.berkeley.edu), `2000.01` SCOP: a structural classification of proteins database, [Nucleic Acids Research](https://academic.oup.com/nar/article-abstract/28/1/257/2384406)
+- [ProteinGym](https://proteingym.org/), `2023.09` Proteingym: Large-scale benchmarks for protein fitness prediction and design, [NeurIPS](https://openreview.net/forum?id=URoZHqAohf)
+- [FLIP](https://benchmark.protein.properties), `2022.01` FLIP: Benchmark tasks in fitness landscape inference for proteins, [bioRxiv](https://www.bioRxiv.org/content/10.1101/2021.11.09.467890v2.abstract)
+- [PEER](https://github.com/DeepGraphLearning/PEER_Benchmark), `2022.09` Peer: a comprehensive and multi-task benchmark for protein sequence understanding, [NeurIPS](https://proceedings.neurips.cc/paper_files/paper/2022/hash/e467582d42d9c13fa9603df16f31de6d-Abstract-Datasets_and_Benchmarks.html)
+- [TAPE](https://github.com/songlab-cal/tape), `2019.09` Evaluating Protein Transfer Learning with TAPE, [NeurIPS](https://proceedings.neurips.cc/paper_files/paper/2019/hash/37f65c068b7723cd7809ee2d31d7861c-Abstract.html)
+
+## 🦠 Genomic Large Language Models (Gene-LLMs)
+
+### General
+
+- `2021.02` DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome [Bioinformatics](https://academic.oup.com/bioinformatics/article/37/15/2112/6128680)
+- `2022.08` MoDNA: motif-oriented pre-training for DNA language model [ACM-BCB](https://dl.acm.org/doi/10.1145/3535508.3545512)
+- `2023.01` Species-aware DNA language modeling [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.01.26.525670.abstract)
+- `2023.01` The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.01.11.523679v3.full.pdf+html)
+- `2023.06` HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution [arXiv](https://arxiv.org/abs/2306.15794)
+- `2023.06` DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome [arXiv](https://arxiv.org/abs/2306.15006)
+- `2023.06` GENA-LM: A Family of Open-Source Foundational Models for Long DNA Sequences [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.06.12.544594v1.abstract)
+- `2023.06` Geneformer: Transfer learning enables predictions in network biology [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.06.12.544594v1.abstract)
+- `2023.07` EpiGePT: a Pretrained Transformer model for epigenomics [bioRxiv](https://www.biorxiv.org/content/10.1101/2023.07.15.549134v1)
+- `2023.08` Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision [bioRxiv](https://arxiv.org/abs/2311.02333)
+- `2023.08` DNAGPT: A Generalized Pre-trained Tool for Versatile DNA Sequence Analysis Tasks [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.07.11.548628v2)
+- `2024.02` GenomicLLM: Exploring Genomic Large Language Models: Bridging the Gap between Natural Language and Gene Sequences [BioRxiv](https://www.biorxiv.org/content/10.1101/2024.02.26.581496v1)
+- `2024.10` A long-context language model for deciphering and generating bacteriophage genomes [Nature Communications](https://www.nature.com/articles/s41467-024-53759-4)
+- `2024.10` CpGPT: a Foundation Model for DNA Methylation [BioRxiv](https://www.biorxiv.org/content/10.1101/2024.10.24.619766v1)
+- `2025-01` GET (general expression transformer): A foundation model of transcription across human cell types. [Nature](https://www.nature.com/articles/s41586-024-08391-z)
+
+### Function Prediction
+
+- `2021.10` Effective gene expression prediction from sequence by integrating long-range interactions [Nature Methods](https://www.nature.com/articles/s41592-021-01252-x)
+- `2022.08` iEnhancer-BERT: A Novel Transfer Learning Architecture Based on DNA-Language Model for Identifying Enhancers and Their Strength [ICIC 2022](https://link.springer.com/chapter/10.1007/978-3-031-13829-4_13)
+- `2022.10` iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations [Genome Biology](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02780-1)
+- `2022.12` iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models [arXiv](https://arxiv.org/abs/2212.01495)
+- `2023.03` miProBERT: identification of microRNA promoters based on the pre-trained model BERT [Briefings in Bioinformatics](https://academic.oup.com/bib/article-abstract/24/3/bbad093/7079709)
+- `2023.07` PLPMpro: Enhancing promoter sequence prediction with prompt-learning based pre-trained language model [Computers in Biology and Medicine](https://www.sciencedirect.com/science/article/abs/pii/S0010482523007254)
+- `2024.02` FGBERT: Function-Driven Pre-trained Gene Language Model for Metagenomics [ArXiv](https://arxiv.org/abs/2402.16901)
+
+### Variants and Evolution Prediction
+
+- `2022.08` DNA language models are powerful predictors of genome-wide variant effects [bioRxiv](https://www.bioRxiv.org/content/10.1101/2022.08.22.504706v3.abstract)
+- `2022.10` GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics [bioRxiv](https://www.bioRxiv.org/content/10.1101/2022.10.10.511571v2)
+- `2023.10` GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.10.10.561776v1.abstract)
+- `2024.12` Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences [ArXiv](https://arxiv.org/abs/2411.04165)
+
+### DNA-Protein Interaction Prediction
+
+- `2023.05` Improving language model of human genome for DNA–protein binding prediction based on task-specific pre-training [Interdisciplinary Sciences: Computational Life Sciences](https://link.springer.com/article/10.1007/s12539-022-00537-9)
+- `2024.04` gLM: Genomic language model predicts protein co-regulation and function [BioRxiv](https://www.biorxiv.org/content/10.1101/2023.04.07.536042v3)
+
+### RNA Prediction
+
+- `2023.02` Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing prediction [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.01.31.526427.abstract)
+- `2023.03` Multiple sequence-alignment-based RNA language model and its application to structural inference [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.03.15.532863v1.abstract)
+- `2023.06` Prediction of Multiple Types of RNA Modifications via Biological Language Model [IEEE/ACM Transactions on Computational Biology and Bioinformatics](https://ieeexplore.ieee.org/abstract/document/10146457/)
+- `2023.07` Uni-RNA: Universal Pre-trained Models Revolutionize RNA Research [bioRxiv](https://www.biorxiv.org/content/10.1101/2023.07.11.548588v1)
+- `2024.02` RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks [ArXiv](https://arxiv.org/abs/2403.00043)
+- `2024.05` RNAErnie: Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning [Nature Machine Intelligence](https://www.nature.com/articles/s42256-024-00836-4)
+
+### Datasets and Benchmarks
+
+- [MGC](https://pubmed.ncbi.nlm.nih.gov/10521335/), `1999.10` The Mammalian Gene Collection [Science](https://www.science.org/doi/abs/10.1126/science.286.5439.455)
+- [GRCh38](https://pubmed.ncbi.nlm.nih.gov/10521335/), `2013.12` Improvements and impacts of GRCh38 human reference on high throughput sequencing data analysis [Genomics](https://www.sciencedirect.com/science/article/pii/S0888754317300058)
+- [690 ChIP-seq](https://academic.oup.com/bioinformatics/article-abstract/32/12/i121/2240609), `2016.06` Convolutional neural network architectures for predicting DNA–protein binding
+- [DeepSEA](http://deepsea.princeton.edu/job/analysis/create/), `2017.04` Predicting effects of noncoding variants with deep learning–based sequence model [Nature Methods](https://www.nature.com/articles/nmeth.3547)
+- [1000 Genomes Project](https://www.internationalgenome.org), `2017.10` A global reference for human genetic variation [Nature](https://www.nature.com/articles/nature15393)
+- [EPDnew](https://epd.expasy.org/epd), `2019.11` EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era [Nucleic Acids Research](https://academic.oup.com/nar/article-abstract/41/D1/D157/1070274)
+- [Panglao Dataset](https://panglaodb.se), `2020.03` PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data [Database](https://academic.oup.com/database/article-abstract/doi/10.1093/database/baz046/5427041)
+- [ExPecto](https://hb.flatironinstitute.org/expecto/?tabId=1), `2020.12` Sequence-based prediction of variants’ effects [Nature Methods](https://www.nature.com/articles/s41592-018-0087-y)
+- [UCSC Genome Database](https://genome.ucsc.edu), `2022.11` The UCSC Genome Browser Database [Nucleic Acids Research](https://academic.oup.com/nar/article/31/1/51/2401563)
+- [BV-BRC](https://www.bv-brc.org), `2023.01` Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR [Nucleic Acids Research](https://academic.oup.com/nar/article-abstract/51/D1/D678/6814465)
+- [Ensembl](https://useast.ensembl.org/index.html), `2023.02` The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools [Nucleic Acids Research](https://academic.oup.com/nar/article-abstract/40/D1/D1202/2903058)
+- [RNAcmap](https://academic.oup.com/bioinformatics/article-abstract/37/20/3494/6281070), `2023.07` RNAcmap: a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis
+- [ENCODE](https://www.encodeproject.org), `2023.09` An integrated encyclopedia of DNA elements in the human genome [Nature](https://www.nature.com/articles/nature11247)
+- [NCBI Genome Database](https://www.ncbi.nlm.nih.gov/genome/), `2023.10` 
+- [TAIR](https://www.arabidopsis.org), `2023.12` The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools [Nucleic Acids Research](https://academic.oup.com/nar/article-abstract/40/D1/D1202/2903058)
+- [VGDB](https://www.ncbi.nlm.nih.gov/genome/viruses/), `2023.12` Viral Genome DataBase: storing and analyzing genes and proteins from complete viral genomes [Bioinformatics](https://academic.oup.com/bioinformatics/article-abstract/16/5/484/192501)
+- [CAGI5](http://www.genomeinterpretation.org/cagi5-challenge.html), `2023.07` CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation [Human Mutation](https://onlinelibrary.wiley.com/doi/abs/10.1002/humu.23873)
+- [Protein–RNA Interaction Prediction](https://academic.oup.com/bib/article-abstract/24/5/bbad307/7252289), `2023.08` A systematic benchmark of machine learning methods for protein–RNA interaction prediction
+- [The Nucleaotide Transformer Benchmark](https://www.biorxiv.org/content/10.1101/2023.01.11.523679.abstract), `2023.09` The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics
+- [GenBench](https://github.com/GenBench/genbench_cbt) `2024.06` GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models [ArXiv](https://arxiv.org/abs/2406.01627)
+- [BEACON](https://github.com/terry-r123/RNABenchmark) BEACON: Benchmark for Comprehensive RNA Tasks and Language Models [ArXiv](https://arxiv.org/abs/2406.10391)
+- [gene-benchmark](https://github.com/BiomedSciAI/gene-benchmark) `2024.12` Does your model understand genes? A benchmark of gene properties for biological and text models [ArXiv](https://arxiv.org/abs/2412.04075)
+
+## Ⓜ️ Multimodal Scientific Large Language Models (MM-Sci-LLMs)
+
+### Molecule&text
+
+- `2021.11` Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries, [EMNLP](https://aclanthology.org/2021.emnlp-main.47/), [Code](https://github.com/cnedwards/text2mol)
+- `2022.02` KV-PLM: A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals, [Nature](https://www.nature.com/articles/s41467-022-28494-3), [Code](https://github.com/thunlp/KV-PLM)
+- `2022.09` MoMu: A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language, [arXiv](https://arxiv.org/abs/2209.05481), [Code](https://github.com/BingSu12/MoMu)
+- `2022.11` MolT5: Translation between Molecules and Natural Language, [arXiv](https://arxiv.org/abs/2204.11817), [Code](https://github.com/blender-nlp/MolT5)
+- `2023.05` Text+Chem T5: Unifying Molecular and Textual Representations via Multi-task Language Modelling, [arXiv](https://arxiv.org/abs/2301.12586), [Code](https://github.com/GT4SD/multitask_text_and_chemistry_t5)
+- `2023.05` DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs, [techRxiv](https://www.techrxiv.org/articles/preprint/DrugChat_Towards_Enabling_ChatGPT-Like_Capabilities_on_Drug_Molecule_Graphs/22945922), [Code](https://github.com/UCSD-AI4H/drugchat)
+- `2023.06` Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language, [arXiv](https://arxiv.org/abs/2303.03363), [Code](https://github.com/ml-jku/clamp)
+- `2023.06` GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning, [bioRxiv](https://www.biorxiv.org/content/10.1101/2023.05.30.542904v2), [Code](https://github.com/zhao-ht/GIMLET)
+- `2023.06` MolReGPT: Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective, [arXiv](https://arxiv.org/abs/2306.06615), [Code](https://github.com/phenixace/MolReGPT)
+- `2023.06` ChatMol: Interactive Molecular Discovery with Natural Language, [arXiv](https://arxiv.org/abs/2306.11976), [Code](https://github.com/Ellenzzn/ChatMol/tree/main)
+- `2023.07` MolXPT: Wrapping Molecules with Text for Generative Pre-training, [ACL](https://aclanthology.org/2023.acl-short.138/)
+- `2023.07` MolFM: A Multimodal Molecular Foundation Model, [arXiv](https://arxiv.org/abs/2307.09484), [Code](https://github.com/PharMolix/OpenBioMed)
+- `2023.08` GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text, [arXiv](https://arxiv.org/abs/2308.06911)
+- `2023.10` GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction, [arXiv](https://arxiv.org/abs/2310.03030), [Code](https://github.com/Suryanarayanan-Balaji/GPT-MolBERTa)
+- `2023.12` MoleculeSTM: Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing, [arXiv](https://arxiv.org/abs/2212.10789), [Code](https://github.com/chao1224/MoleculeSTM/tree/main)
+- `2024.04` Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation, [arXiv](https://arxiv.org/abs/2404.16880), [Code](https://anonymous.4open.science/r/Atomas-03C3)
+- `2024.05` DrugLLM: Open Large Language Model for Few-shot Molecule Generation, [arXiv](https://arxiv.org/abs/2405.06690)
+- `2024.06` 3D-MolT5: Towards Unified 3D Molecule-Text Modeling with 3D Molecular Tokenization, [arXiv](https://arxiv.org/abs/2406.05797)
+- `2024.06` MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction, [arXiv](https://arxiv.org/abs/2406.12950), [Code](https://github.com/NYUSHCS/MolecularGPT)
+- `2024.10` Chemical Language Model Linker: blending text and molecules with modular adapters, [arXiv](https://arxiv.org/abs/2410.20182), [Code](https://github.com/gitter-lab/ChemLML)
+- `2024.11` MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts, [arXiv](https://arxiv.org/abs/2411.14721)
+
+### Protein&text
+
+- `2022.04` ProTranslator: zero-shot protein function prediction using textual description, [arXiv](https://arxiv.org/abs/2204.10286), [Code](https://github.com/HanwenXuTHU/ProTranslator)
+- `2023.02` ProteinDT: A Text-guided Protein Design Framework, [arXiv](https://arxiv.org/abs/2302.04611)
+- `2023.07` ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts, [arXiv](https://arxiv.org/abs/2301.12040), [Code](https://github.com/DeepGraphLearning/ProtST)
+- `2023.07` Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers, [arXiv](https://arxiv.org/abs/2307.14367)
+- `2023.10` InstructProtein: Aligning Human and Protein Language via Knowledge Instruction, [arXiv](https://arxiv.org/abs/2310.03269)
+- `2024.02` ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training, [arXiv](https://arxiv.org/abs/2403.07920), [Code](https://github.com/ProtLLM/ProtLLM)
+- `2024.02` ProtChatGPT: Towards Understanding Proteins with Large Language Models, [arXiv](https://arxiv.org/abs/2402.09649)
+- `2024.02` ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing, [arXiv](https://arxiv.dosf.top/abs/2402.16445), [Code](https://github.com/Lyu6PosHao/ProLLaMA)
+- `2024.04` Functional Protein Design with Local Domain Alignment, [arXiv](https://arxiv.org/abs/2404.16866)
+- `2024.05` ProtT3: Protein-to-Text Generation for Text-based Protein Understanding, [arXiv](https://arxiv.org/abs/2405.12564), [Code](https://github.com/DeepGraphLearning/ProtST)
+- `2024.05` ProteinCLIP: enhancing protein language models with natural language, [bioArXiv](https://www.biorxiv.org/content/10.1101/2024.05.14.594226v1), [Code](https://github.com/wukevin/proteinclip)
+- `2024.07` ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction, [bioArXiv](https://www.biorxiv.org/content/10.1101/2024.04.18.590025.abstract), [Code](https://github.com/MingyuJ666/ProLLM)
+- `2024.08` ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding, [arXiv](https://arxiv.org/abs/2408.11363)
+- `2024.10` ProteinAligner: A Multi-modal Pretraining Framework for Protein Foundation Models, [bioArXiv](https://www.biorxiv.org/content/10.1101/2024.10.06.616870v1.abstract), [Code](https://github.com/Alexiland/ProteinAligner)
+- `2024.10` Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding, [arXiv](https://arxiv.org/abs/2410.03553)
+- `2024.10` TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering, [IEEE 2024](https://ieeexplore.ieee.org/document/10822695), [Code](https://github.com/tsynbio/TourSynbio)
+- `2024.12` ProtDAT: A Unified Framework for Protein Sequence Design from Any Protein Text Description, [arXiv](https://arxiv.org/abs/2412.04069), [Code](https://github.com/GXY0116/ProtDAT)
+
+### Protein&molecule
+
+- `2022.09` ChemBERTaLM: Exploiting pretrained biochemical language models for targeted drug design, [Bioinformatics](https://academic.oup.com/bioinformatics/article/38/Supplement_2/ii155/6702010), [Code](https://github.com/boun-tabi/biochemical-lms-for-drug-design)
+- `2023.03` Deep generative model for drug design from protein target sequence, [Journal of Cheminformatics ](https://link.springer.com/article/10.1186/s13321-023-00702-2), [Code](https://github.com/viko-3/TargetGAN)
+- `2023.06` DrugGPT: A GPT-based Strategy for Designing Potential Ligands Targeting Specific Proteins, [bioRxiv](https://www.bioRxiv.org/content/10.1101/2023.06.29.543848v1), [Code](https://github.com/LIYUESEN/druggpt)
+- `2023.10` DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening, [arXiv](https://arxiv.org/abs/2310.06367)
+- `2023.11` InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery, [arXiv](https://arxiv.org/abs/2311.16208)
+- `2024.10` scChat: A Large Language Model-Powered Co-Pilot for Contextualized Single-Cell RNA Sequencing Analysis, [bioArXiv](https://www.biorxiv.org/content/10.1101/2024.10.01.616063v2.abstract), [Code](https://github.com/li-group/scChat)
+- `2024.10` BioLLMNet: Enhancing RNA-Interaction Prediction with a Specialized Cross-LLM Transformation Network, [bioArXiv](https://www.biorxiv.org/content/10.1101/2024.10.02.616044v2.abstract)
+- `2025.03` Chem42: a Family of chemical Language Models for Target aware Ligand Generation, [arXiv](https://arxiv.org/abs/2503.16563)
+- `2024.11` DrugGen enhances drug discovery with large language models and reinforcement learning, [arXiv](https://arxiv.org/abs/2411.14157), [code](https://github.com/mahsasheikh/DrugGen.git)
+
+### Cell&Text
+
+- `2023.09` Cell2Sentence: Teaching Large Language Models the Language of Biology, [bioRxiv](https://www.biorxiv.org/content/10.1101/2023.09.11.557287v3), [Code](https://github.com/vandijklab/cell2sentence-ft)
+- `2023.10` CELLPLM: PRE-TRAINING OF CELL LANGUAGE MODEL BEYOND SINGLE CELLS, [ICLR](https://www.nature.com/articles/s41467-024-46440-3.pdf), [Code](https://github.com/OmicsML/CellPLM)
+- `2023.10` GenePT: A Simple But Effective Foundation
+Model for Genes and Cells Built From ChatGPT, [bioRxiv](https://arxiv.org/abs/2405.06708), [Code](https://github.com/yiqunchen/GenePT)
+- `2024.02` ChatCell: Facilitating Single-Cell Analysis with Natural Language, [arXiv](https://arxiv.org/abs/2402.08303), [Code](https://github.com/zjunlp/ChatCell)
+- `2024.03` Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis, [Nature Methods](https://www.nature.com/articles/s41592-024-02235-4), [Code](https://github.com/Winnie09/GPTCelltype)
+- `2024.03` Joint Embedding of Transcriptomes and Text Enables Interactive Single-Cell RNA-seq Data Exploration via Natural Language, [ICLR](https://openreview.net/forum?id=yWiZaE4k3K), [Code](https://cellwhisperer.bocklab.org/)
+- `2024.06` LangCell: Language-Cell Pre-training for Cell Identity Understanding, [arXiv](https://arxiv.org/abs/2405.06708), [Code](https://github.com/PharMolix/LangCell)
+- `2024.11` RNA-GPT: Multimodal Generative System for RNA Sequence Understanding, [NeurIPS 2024](https://arxiv.org/abs/2411.08900)
+- `2024.11` BioLLM: A Standardized Framework for Integrating and Benchmarking Single-Cell Foundation Models, [bioArxiv](https://www.biorxiv.org/content/10.1101/2024.11.22.624786v1.abstract), [Code](https://github.com/BGIResearch/BioLLM)
+
+### Comprehensive
+
+- `2022.11` Galactica: A Large Language Model for Science, [arXiv](http://arxiv.org/abs/2211.09085), [Code](https://galactica.org/mission/)
+- `2023.02` BioTranslator: Multilingual translation for zero-shot biomedical classification using BioTranslator, [Nature](https://www.nature.com/articles/s41467-023-36476-2), [Code](https://github.com/HanwenXuTHU/BioTranslatorProject)
+- `2023.05` ChatDrug: ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback, [arXiv](https://arxiv.org/abs/2305.18090), [Code](https://github.com/chao1224/ChatDrug)
+- `2023.08` BioMedGPT：A Pre-trained Language Model for Biomedical Text Mining, [arXiv](https://arxiv.org/abs/2308.09442v2), [Code](https://github.com/PharMolix/OpenBioMed)
+- `2023.08` DARWIN Series: Domain Specific Large Language Models for Natural Science, [arXiv](https://arxiv.org/abs/2308.13565), [Code](https://github.com/MasterAI-EAM/Darwin)
+- `2023.10` BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations, [arXiv](https://arxiv.org/abs/2310.07276), [Code](https://github.com/QizhiPei/BioT5)
+- `2023.11` Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models, [arXiv](https://arxiv.org/abs/2306.08018), [Code](https://github.com/zjunlp/Mol-Instructions)
+- `2024.01` BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs, [arXiv](https://arxiv.org/abs/2310.03320), [Code](https://github.com/RyanWangZf/BioBridge)
+- `2024.02` LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset,  [arXiv](https://arxiv.org/abs/2402.09391), [Page](https://osu-nlp-group.github.io/LLM4Chem/), [Model](https://huggingface.co/osunlp/LlaSMol-Mistral-7B), [Dataset](https://huggingface.co/datasets/osunlp/SMolInstruct)
+- `2024.02` Sequence modeling and design from molecular to genome scale with Evo, [bioRxiv](https://www.biorxiv.org/content/10.1101/2024.02.27.582234v1), [Code](https://github.com/evo-design/evo)
+- `2024.02` BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning, [arXiv](https://arxiv.dosf.top/abs/2402.17810), [Code](https://github.com/QizhiPei/BioT5)
+- `2024.04` MolBind: Multimodal Alignment of Language, Molecules, and Proteins, [arXiv](https://arxiv.org/abs/2403.08167), [Code](https://github.com/tengxiao1/MolBind)
+- `2024.06` Uni-SMART: Universal Science Multimodal Analysis and Research Transformer, [arXiv](https://arxiv.org/abs/2403.10301)
+- `2024.07` SciMind: A Multimodal Mixture-of-Experts Model for Advancing Pharmaceutical Sciences, [ACL Workshop](https://www.biorxiv.org/content/10.1101/2024.07.16.603812v1)
+- `2024.10` BSM: Small but Powerful Biological Sequence Model for Genes and Proteins, [arXiv](https://arxiv.org/abs/2410.11499)
+- `2024.10` MAMMAL: Molecular Aligned Multi-Modal Architecture and Language, [arXiv](https://arxiv.org/abs/2410.22367), [Code](https://github.com/BiomedSciAI/biomed-multi-alignment)
+- `2025.03` PharmAgents: Building a Virtual Pharma with Large Language Model Agents, [arXiv](https://arxiv.org/abs/2503.22164)
+- `2024.11` DrugAgent: Automating AI aided Drug Discovery Programming through LLM Multi Agent Collaboration， [arXiv](https://arxiv.org/abs/2411.15692), [code](https://github.com/anrohanro/DrugAgent.git)
+- `2024.11` DePLM: Denoising Protein Language Models for Property Optimization, [NeurIPS](https://neurips.cc/virtual/2024/poster/95517), [code](https://github.com/HICAI-ZJU/DePLM.git)
+
+### Datasets and Benchmarks
+
+#### Molecule&Text
+
+- [ChEBI-20](https://github.com/cnedwards/text2mol), `2021.11` Text2mol: Cross-modal molecule retrieval with natural language queries, [EMNLP2021](https://aclanthology.org/2021.emnlp-main.47/)
+- [PCdes](https://github.com/thunlp/KV-PLM), `2022.02` A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals, [Nature](https://www.nature.com/articles/s41467-022-28494-3)
+- [MoMu](https://github.com/BingSu12/MoMu/tree/main/data), `2022.09` A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language, [arXiv](https://arxiv.org/abs/2209.05481)
+- [PubChemSTM](https://github.com/chao1224/MoleculeSTM/tree/main/MoleculeSTM/datasets), `2022.12`. Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing, [arXiv](https://arxiv.org/abs/2212.10789)
+- [ChEBL-dia](https://github.com/Ellenzzn/ChatMol/tree/main), `2023.06` ChatMol: Interactive Molecular Discovery with Natural Language, [arXiv](https://arxiv.org/abs/2306.11976)
+- [PubChemQA](https://github.com/PharMolix/OpenBioMed), `2023.08` BioMedGPT：A Pre-trained Language Model for Biomedical Text Mining, [arXiv](https://arxiv.org/abs/2308.09442v2)
+- [MoleculeQA](https://github.com/IDEA-XL/MoleculeQA), `2024.03` MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension, [arXiv](https://arxiv.org/abs/2403.08192)
+- [MolCap-Arena](https://github.com/Genentech/molcap-arena), `2024.11` MolCap-Arena: A Comprehensive Captioning Benchmark on Language-Enhanced Molecular Property Prediction, [arXiv](https://arxiv.org/abs/2411.00737)
+- [TOMG-Bench](https://github.com/phenixace/TOMG-Bench), `2024.12` TOMG-Bench: Evaluating LLMs on Text-based Open Molecule Generation, [arXiv](https://arxiv.org/abs/2412.14642)
+
+#### Protein&Text
+
+- SwissProtCLAP, `2023.02` ProteinDT: A Text-guided Protein Design Framework, [arXiv](https://arxiv.org/abs/2302.04611)
+- [ProtDescribe](https://github.com/DeepGraphLearning/ProtST), `2023.07` ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts, [arXiv](https://arxiv.org/abs/2301.12040)
+- Prot2Text, `2023.07` Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers, [arXiv](https://arxiv.org/abs/2307.14367)
+- [UniProtQA](https://github.com/PharMolix/OpenBioMed), `2023.08` BioMedGPT：A Pre-trained Language Model for Biomedical Text Mining, [arXiv](https://arxiv.org/abs/2308.09442v2)
+- InstructProtein, `2023.10` InstructProtein: Aligning Human and Protein Language via Knowledge Instruction, [arXiv](https://arxiv.org/abs/2310.03269)
+- [ProteinLMDataset,ProteinLMBench](https://huggingface.co/datasets/tsynbio/ProteinLMBench), `2024.06` A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding, [arXiv](https://arxiv.org/abs/2406.05540)
+- [OPI](https://github.com/baaihealth/opi), `2024.11` OPI: An Open Instruction Dataset for Adapting Large Language Models to Protein-Related Tasks, [Neurips 2024](https://openreview.net/forum?id=I4bA7ekJGh)
+
+#### Protein&Molecule
+
+- [DUD-E](https://dude.docking.org/), `2012.06` Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking, [Journal of Medicinal Chemistry](https://pubs.acs.org/doi/10.1021/jm300687e)
+- [BioLiP](https://zhanggroup.org/BioLiP/index.cgi), `2012.10` BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions, [Nucleic Acids Research](https://academic.oup.com/nar/article/41/D1/D1096/1074898)
+- [BindingDB](https://www.bindingdb.org/rwd/bind/index.jsp), `2016.01` BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology, [Nucleic Acids Research](https://academic.oup.com/nar/article/44/D1/D1045/2502601)
+
+#### Cell&Text
+
+- [GEO](http://www.ncbi.nlm.nih.gov/geo/), `2016` The gene expression omnibus database, [Methods Mol Biol](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4944384/pdf/nihms801110.pdf)
+- [The Human Cell Atlas](https://www.humancellatlas.org/), `2017.12` The Human Cell Atlas, [eLife](https://elifesciences.org/articles/27041.pdf)
+- [ARCHS4](https://maayanlab.cloud/archs4/), `2018.04` Massive mining of publicly available RNA-seq data from human and mouse, [Nat Commun](https://www.nature.com/articles/s41467-018-03751-6)
+- [NCBI](https://www.ncbi.nlm.nih.gov/), `2020.10` Database resources of the national center for biotechnology information, [Nucleic Acids Res](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7778943/pdf/gkaa892.pdf)
+- [SRT](https://nanostring.com/products/cosmx-spatial-molecular-imager/ffpe-dataset/nsclc-ffpe-dataset/), `2021.11` High-Plex Multiomic Analysis in FFPE Tissue at Single-Cellular and Subcellular Resolution by Spatial Molecular Imaging, [bioRxiv](https://www.biorxiv.org/content/10.1101/2021.11.03.467020v1)
+- [cellxgene](https://github.com/chanzuckerberg/cellxgene), `2021.04` cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices, [arXiv](https://www.biorxiv.org/content/biorxiv/early/2021/04/06/2021.04.05.438318.full.pdf)
+- [CellTypist](https://github.com/Teichlab/celltypist#interactive-tutorials), `2022.05` Cross-tissue immune cell analysis reveals tissue-specific features in humans, [arXiv](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612735/) 
+- scLibrary, `2024.06` LangCell: Language-Cell Pre-training for Cell Identity Understanding, [arXiv](https://arxiv.org/abs/2405.06708)
+
+#### Comprehensive
+
+- Galactica, `2022.11` Galactica: A Large Language Model for Science, [arXiv](https://arxiv.org/abs/2211.09085)
+- [Scientific Knowledge Dataset](https://github.com/MasterAI-EAM/Darwin), `2023.08` DARWIN Series: Domain Specific Large Language Models for Natural Science, [arXiv](https://arxiv.org/abs/2308.13565)
+- [Mol-Instructions](https://github.com/zjunlp/Mol-Instructions), `2023.10` Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models, [arXiv](https://arxiv.org/abs/2306.08018)
+- [SMolInstruct](https://huggingface.co/datasets/osunlp/SMolInstruct), `2024.02` LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale,  [arXiv](https://arxiv.org/abs/2402.09391)
+- [MolBind-M4](https://github.com/tengxiao1/MolBind), `2024.04` MolBind: Multimodal Alignment of Language, Molecules, and Proteins,  [arXiv](https://arxiv.org/abs/2403.08167)
+
+## 👥 Contributions
+
+### Citation
+
+If you find this repository useful, please cite our paper:
+
+```
+@article{zhang2025Scientific,
+author = {Zhang, Qiang and Ding, Keyan and Lv, Tianwen and Wang, Xinda and Yin, Qingyu and Zhang, Yiwen and Yu, Jing and Wang, Yuhao and Li, Xiaotong and Xiang, Zhuoyi and Zhuang, Xiang and Wang, Zeyuan and Qin, Ming and Zhang, Mengyao and Zhang, Jinlu and Cui, Jiyu and Xu, Renjun and Chen, Hongyang and Fan, Xiaohui and Xing, Huabin and Chen, Huajun},
+title = {Scientific Large Language Models: A Survey on Biological \& Chemical Domains},
+year = {2025},
+issue_date = {June 2025},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+volume = {57},
+number = {6},
+issn = {0360-0300},
+url = {https://doi.org/10.1145/3715318},
+doi = {10.1145/3715318},
+journal = {ACM Comput. Surv.},
+month = feb,
+articleno = {161},
+numpages = {38},
+keywords = {Scientific domain, large language models, protein, molecule, genome}
+}
+```
+
+### Contributors
+
+- Keyan Ding [@dingkeyan93](https://github.com/dingkeyan93)
+- Jing Yu [@jiing17](https://github.com/jiing17)
+- Tianwen Lyu [@smiling-k](https://github.com/smiling-k)
+- Yiwen Zhang [@zhangyiwen2002](https://github.com/zhangyiwen2002)
+- Xinda Wang [@Wwwduojin](https://github.com/Wwwduojin)
+- Qingyu Yin [@MikaStars39](https://github.com/MikaStars39)
+
+### Contact
+
+- Xinda Wang [[email protected]](mailto:[email protected])
+
+![Star History Chart](https://api.star-history.com/svg?repos=HICAI-ZJU/Scientific-LLM-Survey&type=Date)
@@ -0,0 +1,124 @@
+---
+title: "Pr Skill Scan"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/.github/workflows/pr-skill-scan.yml
+upstream_sha: 9c9bd2e9
+imported_at: 2026-06-26
+prompt_class: unknown
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+name: PR Skill Scan
+
+on:
+  pull_request:
+    paths:
+      - "skills/**"
+      - "scan_skills.py"
+      - "scan_pr_skills.py"
+      - "pyproject.toml"
+      - "uv.lock"
+      - ".github/workflows/pr-skill-scan.yml"
+
+permissions:
+  contents: read
+  pull-requests: write
+
+concurrency:
+  group: pr-skill-scan-${{ github.event.pull_request.number }}
+  cancel-in-progress: true
+
+jobs:
+  scan:
+    name: Scan changed skills
+    runs-on: ubuntu-latest
+    timeout-minutes: 60
+
+    steps:
+      - name: Checkout PR
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.pull_request.head.sha }}
+
+      - name: Detect changed skills
+        id: changed
+        run: |
+          set -euo pipefail
+          BASE_SHA="${{ github.event.pull_request.base.sha }}"
+          HEAD_SHA="${{ github.event.pull_request.head.sha }}"
+          echo "Base: $BASE_SHA"
+          echo "Head: $HEAD_SHA"
+
+          # Files added/copied/modified/renamed under skills/<skill>/...
+          CHANGED_FILES=$(git diff --name-only --diff-filter=ACMR "$BASE_SHA" "$HEAD_SHA" -- 'skills/**' || true)
+          echo "Changed files under skills/:"
+          echo "$CHANGED_FILES"
+
+          # Derive unique top-level skill directories and keep only those that still exist with a SKILL.md
+          SKILL_DIRS=$(echo "$CHANGED_FILES" \
+            | awk -F/ 'NF>=2 && $1=="skills" {print $1 "/" $2}' \
+            | sort -u)
+
+          EXISTING=""
+          for d in $SKILL_DIRS; do
+            if [ -f "$d/SKILL.md" ]; then
+              EXISTING="$EXISTING $d"
+            fi
+          done
+          EXISTING=$(echo "$EXISTING" | xargs || true)
+
+          echo "Skill dirs to scan: '$EXISTING'"
+          echo "skill_dirs=$EXISTING" >> "$GITHUB_OUTPUT"
+
+      - name: Set up uv
+        if: steps.changed.outputs.skill_dirs != ''
+        uses: astral-sh/[email protected]
+        with:
+          enable-cache: true
+          cache-dependency-glob: uv.lock
+          python-version: "3.13"
+
+      - name: Install dependencies
+        if: steps.changed.outputs.skill_dirs != ''
+        run: uv sync --python 3.13
+
+      - name: Run scanner on changed skills
+        if: steps.changed.outputs.skill_dirs != ''
+        id: scan
+        env:
+          SKILL_SCANNER_LLM_API_KEY: ${{ secrets.SKILL_SCANNER_LLM_API_KEY }}
+          SKILL_SCANNER_LLM_MODEL: ${{ vars.SKILL_SCANNER_LLM_MODEL || 'claude-sonnet-4-6' }}
+        run: |
+          uv run python scan_pr_skills.py \
+            --output pr_scan_comment.md \
+            --fail-on HIGH \
+            ${{ steps.changed.outputs.skill_dirs }}
+
+      - name: Prepare no-op comment
+        if: steps.changed.outputs.skill_dirs == ''
+        run: |
+          cat > pr_scan_comment.md <<'EOF'
+          <!-- skill-security-scan -->
+          ## 🛡️ Skill Security Scan
+
+          No skill directories (with a `SKILL.md`) were changed in this PR — nothing to scan.
+          EOF
+
+      - name: Upload scan comment as artifact
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: pr-skill-scan-comment
+          path: pr_scan_comment.md
+          if-no-files-found: ignore
+
+      - name: Post or update PR comment
+        if: always() && hashFiles('pr_scan_comment.md') != ''
+        uses: marocchino/sticky-pull-request-comment@v2
+        with:
+          header: skill-security-scan
+          path: pr_scan_comment.md
@@ -0,0 +1,114 @@
+---
+title: "Readme"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/PatWalters/resources_2025/blob/fdc9ddb3/README.md
+upstream_sha: fdc9ddb3
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+## Machine Learning in Drug Discovery Resources 2025
+
+### Books
+
+[Drug Design: From Structure and Mode-of-Action to Rational Design Concepts](https://www.amazon.com/Drug-Design-Structure-Mode-Action/dp/3662689979)
+As cheminformatics practitioners, we need to understand the drug design process.  This book, written by Prof. Gerhard Klebe, a pioneer in the field, provides an excellent overview of numerous drug design approaches.
+
+[Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X)
+Programming and data science are critical elements of cheminformatics.  This book, written by Wes McKinney, the author of the widely used Pandas library, provides a great starting point for learning Python and applying it in data science.
+
+[Data Science from Scratch: First Principles with Python](https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/1492041130/) This book provides another great introduction to data science. It provides an introduction to several critical topics, including Python, Statistics, Probability, Machine Learning, Clustering, and Databases.
+
+[Statistics in a Nutshell: A Desktop Quick Reference](https://www.amazon.com/Statistics-Nutshell-Desktop-Quick-Reference/dp/1449316824/)
+[Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python](https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-learning/dp/1801819319/)
+To effectively apply cheminformatics, one needs a solid grasp of statistics.  This book provides a good overview with code examples.
+
+[Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python](https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-learning/dp/1801819319/)
+Machine learning (ML) has become an integral component on cheminformatics.  This book provides a fantastic introduction to more traditional ML approaches and recent advances in deep learning.
+
+
+### Datasets
+
+You'll notice the conspicuous absence of two widely used datasets, [MoleculeNet](https://moleculenet.org/) and the [Therapeutic Data Commons (TDC)](https://tdcommons.ai/), from this list.  Both of these datasets are highly flawed and should not be used.  For more on the reasons why, please
+consult this [blog post](https://practicalcheminformatics.blogspot.com/2023/08/we-need-better-benchmarks-for-machine.html). 
+
+[OpenADMET](https://openadmet.org) seeks to proactively characterize the chemical space accessible to
+ADMET-associated proteins (“anti-targets”). By applying recent advances in experimental and computational techniques, a
+comprehensive open library of experimental and structural datasets will be generated. It's early days for OpenADMET, but
+knowing the folks involved, I'm highly optimistic. 
+
+[AIRCHECK](https://aircheck.ai) is a platform that provides access to a large collection of high-quality datasets for drug discovery and
+development. The datasets are curated from various sources and are available in a standardized format. The current
+focus appears to be on DNA-encoded library (DEL) data.
+
+[Polaris](https://polarishub.io) aims to improve the state of benchmarking so ML can have a more significant impact on real-world drug discovery
+scenarios. To start, Polaris hopes to provide a single source of truth that aggregates and provides simple access to
+datasets & benchmarks.
+
+[PLINDER](https://plinder.sh) is an academic-industry collaboration to collect and organize protein-ligand interaction data. The effort is
+driven by VantAI, NVIDIA, the Computational Structural Biology group at the University of Basel & SIB Swiss Institute
+of Bioinformatics (co-organizers of CASP), and MIT. PLINDER aims to provide a gold standard dataset and evaluations
+to push the field of computational protein-ligand interactions prediction forward.
+
+### Blogs
+
+[Eric J Ma's Website](https://ericmjl.github.io/)
+Eric's blog provides an excellent introduction to the application of cutting-edge informatics in drug discovery.
+
+[Oxford Protein Informatics Group (OPIG)](https://www.blopig.com/blog)
+This blog contains a lot of great [Bio|Chem]informatics content, chock-full of code. 
+
+[Charlie’s Substack](https://harrisbio.substack.com/)
+Charlie Harris writes about applications of AI in drug discovery. Most recently, his posts have focused on efforts
+to reproduce AlphaFold3.
+
+[Mogan Thomas' Cheminformatics Blog](https://cheminformantics.blogspot.com/)
+This one is new, but it looks promising based on the first post. 
+
+[Jon Swain's Blog](https://jonswain.github.io/)
+Jon Swain, a second-generation Cheinformatics blogger, has a great set of Jupyter notebooks demonstrating key concepts. 
+
+[Practical Cheminformatics](https://practicalcheminformatics.blogspot.com/)
+This is a blog where I post once a month or so. These posts typically contain code demonstrating various aspects
+of cheminformatics; clustering, machine learning, data visualization, etc. I occasionally post
+opinions on things like AI and getting a job.
+
+[Is Life Worth Living](https://iwatobipen.wordpress.com/)
+A great blog from Iwatobipen (aka pen), whose posts are
+chock-full of great code examples. Pen always seems to be up on the latest methods and posts interesting examples on various topics ranging from quantum chemistry to machine learning.
+
+[The RDKit Blog](http://rdkit.blogspot.com/)
+Greg Landrum is the primary contributor to and BDFL of the RDKit. In
+addition to the latest and greatest features in the RDKit, Greg's posts also touch on a number of key issues in
+Cheminformatics, such as dealing with unbalanced datasets and the impact of fingerprint folding on similarity searching.
+
+[Models to molecules](https://driesvr.github.io/)
+A new blog by Dries Van Rompaey that is off to a great start. 
+
+### Tutorials
+
+[Practical Cheminformatics Tutorials](https://github.com/PatWalters/practical_cheminformatics_tutorials)
+I put together this collection of Jupyter notebooks to demonstrate various aspects of cheminformatics and
+machine learning. The notebooks illustrate a range of topics from cheminformatics basics to more advanced
+machine learning. The tutorials all use open source software and can run on Google Colab without installing software
+locally.
+
+[TeachOpenCADD](https://github.com/volkamerlab/TeachOpenCADD)
+A great set of tutorials from Andrea Volkamer's group that use open-source software to teach Computer-Aided Drug Design concepts, including molecular similarity, applications of machine learning, and pharmacophore analysis.
+
+[The RDKit Cookbook](https://www.rdkit.org/docs/Cookbook.html)
+A terrific resource that provides "recipes" for a number of common tasks.
+
+[Vina Colab Tutorials](https://autodock-vina.readthedocs.io/en/latest/colab_examples.html)
+A tutorial set shows how to run Autodock Vina and the associated protein and ligand setup utilities on Google Colab.
+
+[GNNs for Chemists](https://github.com/HFooladi/GNNs-For-Chemists)
+A great introduction to graph neural networks (GNNs) by Hosein Fooladi.
+
+[PDB-101 from the RCSB PDB](https://pdb101.rcsb.org/train/training-events)
+The Protein Databank (PDB) has a wide range of tutorials available.  The Python scripting tutorials are very good.
+
@@ -0,0 +1,61 @@
+---
+title: "Contributing Guidelines"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/yboulaamane/awesome-drug-discovery/blob/b8fbd716/CONTRIBUTING.md
+upstream_sha: b8fbd716
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Contributing Guidelines
+
+We welcome and appreciate all contributions!  
+This repository is a curated list of resources, so maintaining consistency and quality is key.
+
+---
+
+## How to Contribute
+
+1. **Create or Update a Section**  
+   - If your resource doesn’t fit an existing section, create a new one.  
+   - Provide a short description for the section.  
+   - Add the section title to the Contents.
+
+2. **Add a Resource**  
+   - Check existing entries to avoid duplicates.  
+   - Format new entries as:  
+     ```
+     * [project-name](http://example.com/) - Short description ending with a period.
+     ```
+   - Keep descriptions short and clear (one sentence).  
+   - If the tool has unique features, list them in bullet points beneath the main entry.
+
+3. **Quality Checks**  
+   - Verify spelling and grammar.  
+   - Ensure your text editor removes trailing spaces automatically.  
+   - Keep formatting consistent with the rest of the document.
+
+4. **Submit Your Changes**  
+   - Make a pull request with a clear title and summary of changes.
+
+---
+
+## Style Guide
+
+- **Links:** Always use Markdown link format `[name](url)`  
+- **Descriptions:**  
+  - Concise (max one sentence)  
+  - End with a period  
+  - Avoid marketing language  
+- **Bullet Points:**  
+  - Optional; only for notable functionality or features  
+- **Consistency:** Follow the structure of existing entries in similar sections.
+
+---
+
+Thank you for helping improve this resource!  
+Every contribution helps make this list more valuable for the community.
@@ -0,0 +1,449 @@
+---
+title: "Awesome Drug Discovery [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/yboulaamane/awesome-drug-discovery/blob/b8fbd716/README.md
+upstream_sha: b8fbd716
+imported_at: 2026-06-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Awesome Drug Discovery [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
+A meticulously curated resource list focused on computational methods for drug discovery.
+
+> Drug discovery is the process by which new candidate medications are identified, designed, and developed using experimental, computational, and informational techniques to address complex challenges in biology, chemistry, and medicine. — [Wikipedia](https://en.wikipedia.org/wiki/Drug_discovery)
+
+---
+
+## Contents
+- [Databases and Chemical Libraries](#databases-and-chemical-libraries)  
+  - [General Compound Libraries](#general-compound-libraries)  
+  - [Natural Product Libraries](#natural-product-libraries)  
+  - [Bioactivity Databases](#bioactivity-databases)  
+- [Target and Protein Data](#target-and-protein-data)  
+  - [Protein Structures](#protein-structures)  
+  - [Binding Site and Pocket Detection](#binding-site-and-pocket-detection)  
+  - [Protein Engineering and Modeling](#protein-engineering-and-modeling)
+- [Network Pharmacology](#network-pharmacology)  
+- [Ligand Design and Optimization](#ligand-design-and-optimization)  
+  - [Pharmacophore Modeling](#pharmacophore-modeling)  
+  - [QSAR and Descriptor Tools](#qsar-and-descriptor-tools)  
+  - [Descriptor and Featurization Tools](#descriptor-and-featurization-tools)
+  - [Molecular Property Prediction](#molecular-property-prediction)  
+  - [Fragment-Based Drug Design](#fragment-based-drug-design)  
+- [Virtual Screening and Docking](#virtual-screening-and-docking)  
+- [Interaction Analysis and Visualization](#interaction-analysis-and-visualization)  
+- [Molecular Dynamics and Simulation](#molecular-dynamics-and-simulation)  
+  - [Engines](#engines)  
+  - [Topology and Force Field Tools](#topology-and-force-field-tools)  
+  - [Analysis Tools](#analysis-tools)  
+- [Synthesis and Retrosynthesis Planning](#synthesis-and-retrosynthesis-planning)  
+- [Specialized Modalities](#specialized-modalities)  
+  - [PROTACs and Ternary Complexes](#protacs-and-ternary-complexes)  
+  - [Peptide Design](#peptide-design)  
+- [Machine Learning and AI](#machine-learning-and-ai)  
+  - [Core Libraries](#core-libraries)  
+  - [Chemistry-focused ML Frameworks](#chemistry-focused-ml-frameworks)  
+  - [Pretrained Models](#pretrained-models)  
+  - [AutoML and Optimization](#automl-and-optimization)  
+  - [Molecule Standardization](#molecule-standardization)  
+- [Utility and Workflow Tools](#utility-and-workflow-tools)  
+- [Learning Resources](#learning-resources)  
+  - [Free Courses](#free-courses)  
+  - [Blogs](#blogs)  
+  - [Instructional Notebooks](#instructional-notebooks)
+- [Labs and Research Groups](#labs-and-research-groups)
+
+---
+
+## Databases and Chemical Libraries
+
+### General Compound Libraries
+- [DrugBank](https://go.drugbank.com/) - Comprehensive data on approved and investigational drugs.
+- [ZINC](https://zinc.docking.org/) - Free compounds for screening.  
+- [ChemSpider](http://www.chemspider.com/) - Chemical structures and data.  
+- [DrugSpaceX](https://drugspacex.simm.ac.cn/) - Chemical and biological spaces.  
+- [Mcule](https://mcule.com/) - Virtual screening platform with purchasable compounds.  
+- [Otava Chemicals](https://www.otavachemicals.com/) - Screening compounds and building blocks.  
+- [Vitas-M Laboratory](https://vitasmlab.biz/) - Chemical libraries for HTS and lead discovery.
+- [Eximed](https://eximedlab.com/Screening-Compounds.html) - 60k+ compounds for virtual screening.
+- [OTAVA NP-like Library](https://otavachemicals.com/sdf) - Screening compounds for prompt delivery.
+- [Ambinter](https://www.ambinter.com/) - 40M+ compounds for HTS, building blocks, and a wide selection of fragments and natural products.
+- [VAST Chemical Space](https://www.aifchem.com/vast) - 4.6 billion synthetically accessible compounds for virtual screening and hit expansion.
+
+### Natural Product Libraries
+- [ZINC15 Natural Products](https://zinc15.docking.org/substances/subsets/natural-products/) - 200k+ natural compounds.  
+- [COCONUT](https://coconut.naturalproducts.net/) - 400k+ natural products.  
+- [LOTUS](https://lotus.naturalproducts.net/) - Annotated molecular data with sourcing organisms.  
+- [NPASS](http://bidd.group/NPASS/index.php) - 94k activity-species links.  
+- [ANPDB](https://phabidb.vm.uni-freiburg.de/anpdb/) - 27k+ African medicinal plant compounds.  
+- [SANCDB](https://sancdb.rubi.ru.ac.za/) - Natural compounds from the plant and marine life in and around South Africa.  
+- [CMNPD](https://www.cmnpd.org/) - 31k+ marine natural products.  
+- [SistematX](https://sistematx.ufpb.br/) - 8k+ secondary metabolites.  
+- [CoumarinDB](https://yboulaamane.github.io/CoumarinDB/) - A manually curated database on coumarins from plants.  
+- [ArtemisiaDB](https://yboulaamane.github.io/ArtemisiaDB/) - Artemisia genus compounds.  
+- [BIAdb](https://webs.iiitd.edu.in/raghava/biadb/type.php?tp=natural) - A database for benzylisoquinoline alkaloids.  
+- [IMPPAT](https://cb.imsc.res.in/imppat/home) - Phytochemicals from Indian medicinal plants.  
+- [NP-MRD](https://np-mrd.org/natural_products) - 280k+ NMR-based NP studies.  
+- [IBS Natural Compounds](https://www.ibscreen.com/natural-compounds) - 60k+ compounds.  
+- [PhytoHub](https://phytohub.eu/) - Dietary phytochemicals and metabolites.  
+- [Dr. Duke's Phytochemical DB](https://phytochem.nal.usda.gov/) - Plant compounds and uses.  
+- [CyanoMetDB](https://zenodo.org/records/13854577) - Over 3,000 cyanobacterial metabolites.  
+- [Seaweed Metabolite DB](https://www.swmd.co.in/) - Marine algae compounds.  
+- [FooDB](https://foodb.ca/) - A comprehensive resource on food constituents.  
+
+### Bioactivity Databases
+- [ChEMBL](https://www.ebi.ac.uk/chembl/) - Bioactivity and ADMET data.  
+- [SureChEMBL](https://www.surechembl.org/) - Patent chemistry search.  
+- [BindingDB](https://www.bindingdb.org/) - Binding affinities for biomolecules.  
+- [PubChem](https://pubchem.ncbi.nlm.nih.gov/) - Structures, properties, and bioassays.  
+- [PDBbind](http://www.pdbbind.org.cn/index.php) - Protein-ligand affinity data.  
+- [BRENDA](https://www.brenda-enzymes.org/) - Enzyme properties and functions.  
+- [ExCAPE-DB](https://solr.ideaconsult.net/search/excape/) - A large-scale chemogenomics database.  
+- [Therapeutics Data Commons](https://tdcommons.ai/) - AI/ML-ready datasets and learning tasks for therapeutics.
+- [Therapeutic Target Database (TTD)](https://idrblab.net/ttd/) - Drug targets with linked diseases and compounds.
+- [Aircheck Datasets](https://aircheck.ai/datasets) - Curated DEL datasets for AI‑driven drug discovery, enabling benchmarking and model development.
+- [canSAR](https://cansar.ai/) - Integrative cancer knowledgebase aggregating molecular, genetic, and structural data for drug target identification.
+- [CDD Vault](https://www.collaborativedrug.com/public-access-cdd-vault) - Hosted informatics platform providing public access to aggregated drug discovery data.
+- [ClinicalTrials.gov](https://clinicaltrials.gov/) - Comprehensive registry and results database for clinical studies involving human participants.
+- [HSADab](https://github.com/proszxppp/HSADab) - Database of binding thermodynamics, structures, and docking data for human serum albumin.
+
+---
+
+## Target and Protein Data
+
+### Protein Structures
+- [RCSB PDB](https://www.rcsb.org/) - Repository for macromolecular structures.
+- [PDBe](https://www.ebi.ac.uk/pdbe/) - European counterpart to RCSB PDB.
+- [OPM](https://opm.phar.umich.edu/) - Orientation of proteins in membranes.
+- [UniProt](https://www.uniprot.org/) - Protein sequences, structures, and functions.
+- [InterPro](https://www.ebi.ac.uk/interpro/) - Protein classification and domain prediction.
+- [AlphaFold DB](https://alphafold.ebi.ac.uk/) - Predicted structures from AlphaFold.
+- [Proteopedia](https://proteopedia.org/wiki/index.php/Main_Page) - Interactive protein visualizations.
+- [Pfam](https://pfam.xfam.org/) - Collection of protein families represented by multiple sequence alignments and hidden Markov models.
+- [Human Protein Atlas](https://www.proteinatlas.org/) - Spatial mapping of all human proteins across tissues and cells.
+
+### Binding Site and Pocket Detection
+- [PrankWeb](https://prankweb.cz/) - Pocket prediction and analysis.
+- [CASTp](http://sts.bioe.uic.edu/castp/index.html?2r7g) - Pocket geometry and volume analysis.
+- [CavityPlus](http://www.pkumdl.cn:8000/cavityplus/index.php#/) - Pocket detection and druggability.
+- [CaverWeb](https://loschmidt.chemi.muni.cz/caverweb/) - Tunnel and channel detection.
+- [PASSer](https://passer.smu.edu/) - Allosteric site prediction.
+- [Pocket Binding Site Prediction](https://github.com/MariaPau03/Pocket_Binding_Site_Prediction) - ML-based tool for predicting binding pockets and active sites on protein structures.
+- [Protplex](https://protplex.com/) - Semantic search engine for the PDB enabling multidimensional queries on structures and binding pockets.
+
+### Protein Engineering and Modeling
+- [DynaMut](https://biosig.lab.uq.edu.au/dynamut/) - Predicts mutation-induced stability changes.
+- [SWISS-MODEL](https://swissmodel.expasy.org/) - A fully automated protein structure homology-modeling server.
+- [MODELLER](https://salilab.org/modeller/) - A software for homology or comparative modeling of protein structures.
+- [PDBFixer](https://github.com/openmm/pdbfixer) - Repairs PDB files by adding missing atoms, residues, and hydrogens for MD simulations.
+- [OpenFold Portal](https://portal.openfold.omsf.io/) - Cloud portal for predicting 3D protein structures using the open-source OpenFold model.
+- [Melodia](https://github.com/rwmontalvao/Melodia_py) - Python library for analyzing and comparing protein structure shapes via differential geometry.
+
+---
+
+## Network Pharmacology
+- [GeneCards](https://www.genecards.org/) - Human gene database with genomic, proteomic, and clinical data.
+- [SwissTargetPrediction](http://www.swisstargetprediction.ch/) - Predicts targets of small molecules via similarity-based screening.
+- [STITCH](https://stitch.embl.de/) - Integrates chemical–protein interactions across organisms.
+- [STRING](https://string-db.org/) - A database of known and predicted protein–protein interactions.
+- [Cytoscape](https://cytoscape.org/) - Visualizes and analyzes molecular interaction networks.
+- [Open Targets](https://platform.opentargets.org/) - Integrative platform for therapeutic target identification.
+- [OmicsNet](https://www.omicsnet.ca/) - Builds multi-omics networks for systems biology.
+- [DisGeNET](https://disgenet.com/) - Curated gene–disease associations for network analysis.
+- [PharmMapper](https://www.lilab-ecust.cn/pharmmapper/) - Identifies potential targets via reverse pharmacophore mapping.
+- [ChEA3](https://maayanlab.cloud/chea3/) - Transcription factor enrichment tool integrating ChIP-seq, co-expression, and perturbation datasets.
+- [miRDB](https://mirdb.org/) - Predicts functional microRNA targets using machine learning and high-throughput data.
+- [Venny 2.1](https://bioinfogp.cnb.csic.es/tools/venny/) - A web tool for comparing lists using Venn diagrams.
+- [OMIM](https://www.omim.org/) - Authoritative compendium of human genes and their relationship to genetic variation and phenotypic expression.
+- [PharmGKB](https://pgx-db.org/target_lookup/) - Pharmacogenomics resource exploring genetic variation impacts on drug response and molecular targets.
+- [Polypharmacology Browser PPB3](https://ppb3.gdb.tools/) - Deep learning tool predicting off-target effects and polypharmacology for bioactive molecules.
+- [Drug-Target Interaction Explorer](https://github.com/yashhhhhhhhh504/Drug-Target-Interaction-Explorer) - Dashboard for exploring and visualizing drug-target interaction networks.
+
+---
+
+## Ligand Design and Optimization
+
+### Pharmacophore Modeling
+- [ZINCPharmer](http://zincpharmer.csb.pitt.edu/) - Pharmacophore screening.
+- [Pharmit](https://pharmit.csb.pitt.edu/) - Interactive pharmacophore modeling.
+- [AnchorQuery](http://anchorquery.csb.pitt.edu/) - Pharmacophore-based search engine specialized in protein–protein interaction sites.
+
+### QSAR and Descriptor Tools
+- [QSAR Toolbox](https://qsartoolbox.org/) - Hazard assessment and QSAR.
+- [OCHEM](https://ochem.eu/home/show.do) - QSAR model building and prediction.
+- [ChemMaster](https://crescent-silico.com/chemmaster/) - QSAR and cheminformatics suite.
+- [3D-QSAR](https://www.3d-qsar.com/) - Web resources for 3D QSAR modeling.
+- [QSAR-Co](https://sites.google.com/view/qsar-co/) - Robust multitarget QSAR modeling.
+- [DataWarrior](https://openmolecules.org/datawarrior/) - Free software for chemical analysis, QSAR, and visualization.
+- [KNIME](https://www.knime.com/) - Workflow platform for cheminformatics and ML integration.
+- [pyADA](https://github.com/jeffrichardchemistry/pyADA) - Assesses the applicability domain of molecular fingerprints via similarity-based thresholds for QSAR validation.
+
+### Descriptor and Featurization Tools
+- [RDKit](https://www.rdkit.org/) - Open-source cheminformatics toolkit with descriptor, fingerprint, and molecular manipulation support.  
+- [PaDEL-Descriptor](http://www.yapcwsoft.com/dd/padeldescriptor/) - Java tool for calculating molecular descriptors and fingerprints.  
+- [Mordred](https://github.com/mordred-descriptor/mordred) - Python library with 1800+ molecular descriptors.  
+- [CDK](https://cdk.github.io/) - Java cheminformatics library with descriptor calculators.  
+- [alvaDesc](https://www.alvascience.com/alvadesc/) - Commercial software for molecular descriptors and fingerprints.  
+- [MolFeat](https://molfeat.datamol.io/) - Python package for molecular featurization and embeddings.
+- [Dragon](https://www.talete.mi.it/products/dragon_description.htm) - Commercial molecular descriptor calculator (widely cited).
+- [ChemDescriptor](https://github.com/darkreactions/chemdescriptor) - Open-source tool for generating chemical descriptors and fingerprints, supporting cheminformatics workflows.
+
+### Molecular Property Prediction
+- [SwissADME](http://www.swissadme.ch/) - Drug-likeness and PK.
+- [pkCSM](https://biosig.lab.uq.edu.au/pkcsm/) - ADMET property prediction.
+- [DeepPK](https://biosig.lab.uq.edu.au/deeppk/) - DL-based pharmacokinetics.
+- [admetSAR 2.0](https://lmmd.ecust.edu.cn/admetsar2/) - Comprehensive ADMET.
+- [ADMETlab 2.0](https://admetmesh.scbdd.com/) - PK, toxicity and drug-likeness.
+- [ProTox-II](https://tox-new.charite.de/protox_II/) - Toxicity predictions.
+- [PreADMET](https://preadmet.webservice.bmdrc.org/) - PK property predictions.
+- [FAF-Drugs](https://bioserv.rpbs.univ-paris-diderot.fr/services.html) - ADMET filtering.
+- [Admetboost](https://ai-druglab.smu.edu/admet) - ML-based ADMET prediction.
+- [MetaPredict](http://metapredict.icoa.fr/) - Predict molecular properties from structure.
+- [ADMET-AI](https://admet.ai.greenstonebio.com/) - A web-based tool for predicting ADMET properties based on Chemprop-RDKit models trained on datasets from the TDC.
+
+### Fragment-Based Drug Design
+
+- [SwissSidechain](https://www.swisssidechain.ch/) - Fragment and linker library for small molecule design.
+- [BoBER](http://bober.insilab.org/) - Bioisosteric replacements for lead optimization.
+- [FragBuilder](https://github.com/andersx/fragbuilder) - Python API for building peptide-like and small molecule fragments.  
+- [SeeSAR](https://www.biosolveit.de/SeeSAR/) - Fragment growing and linking software (free academic version).
+- [Enamine Fragment Libraries](https://enamine.net/compound-libraries/fragment-libraries) - Large curated collection of diverse fragments for FBDD.
+- [FragmentFinder](https://github.com/1JELC1/FragmentFinder) - Computational tool for identifying and matching structural fragments in drug discovery workflows.
+
+---
+
+## Virtual Screening and Docking
+- [OpenBabel](https://openbabel.org/index.html) - Format conversion and ligand prep.
+- [Meeko](https://github.com/forlilab/Meeko) - Prepares ligands/receptors for AutoDock by assigning partial charges and atom types.
+- [MolScrub](https://github.com/forlilab/molscrub) - Enumerates tautomers, pH states, and conformers for docking and structure-based modeling.
+- [MGLTools](https://ccsb.scripps.edu/mgltools/) - Structure preparation.
+- [AutoDockTools](https://autodocksuite.scripps.edu/adt/) - AutoDock GUI.
+- [AutoDock Vina](https://vina.scripps.edu/) - Popular docking software.
+- [AutoDock-GPU](https://github.com/ccsb-scripps/AutoDock-GPU) - GPU-accelerated version of AutoDock for faster ligand-receptor docking.
+- [DiffDock](https://github.com/gcorso/DiffDock) - Deep learning-based docking tool that predicts ligand poses directly from protein structures using diffusion models.  
+- [EasyDockVina2](https://github.com/S3cr3t-SDN/EasyDockVina2) - Vina automation.  
+- [Webina](https://durrantlab.pitt.edu/webina/) - Web-based Vina.
+- [Smina](https://github.com/mwojcikowski/smina) - Vina fork with extra features.
+- [Gnina](https://github.com/gnina/gnina) - CNN-scoring docking.
+- [EasyDock](https://github.com/ci-lab-cz/easydock) - Vina/Smina pipeline.
+- [HADDOCK](https://wenmr.science.uu.nl/haddock2.4/) - Flexible docking suite.
+- [PandaDock](https://github.com/pritampanda15/PandaDock) - Python docking tool.
+- [ZDOCK](https://zdock.wenglab.org/) - Protein-protein docking.
+- [ClusPro](https://cluspro.org/) - Protein-protein docking server.
+- [pyDockWEB](https://life.bsc.es/pid/pydockweb/) - Electrostatics-based docking.
+- [SwissDock](https://www.swissdock.ch/) - Web docking for beginners.
+- [MzDOCK](https://github.com/Muzatheking12/MzDOCK) - GUI docking pipeline.
+- [Uni-Mol Docking V2](https://www.bohrium.com/apps/unimoldockingv2/job?type=app) - AI-assisted docking.
+- [Vina on Colab](https://autodock-vina.readthedocs.io/en/latest/colab_examples.html) - Run Vina in Google Colab.
+- [MetalDock](https://metaldock.readthedocs.io/en/latest/) - A Python-based tool designed for the docking of metal-organic compounds to proteins, DNA, or other biomolecules.
+- [Chopdock](https://github.com/JanoschMenke/chopdock) - Molecular docking and cheminformatics tool for structural interaction analysis and fragment-based design.
+- [Boltzmann Maps](https://boltzmannmaps.com/) - Web application for structure-guided drug design using pre-computed water and chemical fragment maps.
+
+---
+
+## Interaction Analysis and Visualization
+- [PLIP](https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index) - Protein-ligand interaction profiling.
+- [GetContacts](https://getcontacts.github.io/index.html) - Compute and visualize noncovalent interactions from structures and MD trajectories.
+- [LigPlot+](https://www.ebi.ac.uk/thornton-srv/software/LigPlus/) - 2D interaction diagrams.
+- [Discovery Studio Visualizer](https://discover.3ds.com/discovery-studio-visualizer-download) - Advanced visualization.
+- [PyMOL](https://www.pymol.org/) - Python-based molecular visualization software.
+- [UCSF ChimeraX](https://www.rbvi.ucsf.edu/chimerax/) - A molecular visualization program with emphasis on structural biology.
+- [Avogadro](https://avogadro.cc/) - Cross-platform molecular editor and visualizer featuring an extensible plugin system.
+- [xyzrender](https://github.com/aligfellow/xyzrender) - CLI for producing publication-quality molecular graphics, GIFs, and SVGs from coordinate files.
+- [pymol-sifts](https://github.com/connyyu/pymol_sifts/) - PyMOL plugin for integrating and visually mapping SIFTS structural and sequence data.
+
+---
+
+## Molecular Dynamics and Simulation
+
+### Engines
+- [GROMACS](https://www.gromacs.org/) - Fast, scalable MD engine optimized for biomolecular simulations and energy minimization.
+- [OpenMM](https://openmm.org/) - Flexible MD toolkit with GPU acceleration and Python bindings.
+- [LAMMPS](https://www.lammps.org/) - Classical MD simulator for materials science and soft matter.
+- [NAMD](https://www.ks.uiuc.edu/Research/namd/) - Highly parallel MD engine tailored for large biomolecular systems.
+- [AMBER](https://ambermd.org/) - Suite for biomolecular simulations and free energy calculations.
+- [Desmond](https://www.deshawresearch.com/resources.html) - GPU-accelerated MD engine for high-performance simulations.
+
+### Topology and Force Field Tools
+- [CGenFF](https://cgenff.umaryland.edu/) - CHARMM force field parametrization of drug-like molecules.
+- [SwissParam](https://www.swissparam.ch/) - Rapid generation of CHARMM-compatible parameters for small organic molecules.
+- [ATB](https://atb.uq.edu.au/) - Automated topology builder and repository for classical force field parameters.
+- [CHARMM-GUI](https://www.charmm-gui.org/) - Web-based interface for building complex biomolecular systems and generating MD input files.
+- [LigParGen](https://zarbi.chem.yale.edu/ligpargen/) - Automated OPLS-AA parameter generator for organic ligands.
+
+### Analysis Tools
+- [MD DaVis](https://md-davis.readthedocs.io/en/latest/index.html) - Interactive visualization and analysis of MD trajectories.
+- [iMod](https://imods.iqfr.csic.es/) - Normal Mode Analysis toolkit using internal coordinates.
+- [MolAiCal](https://molaical.github.io/) - Web-based platform for binding free energy calculations using MM/PBSA and MM/GBSA methods.
+- [gmx_MMPBSA](https://valdes-tresanco-ms.github.io/gmx_MMPBSA/dev/) - Port of AMBER MMPBSA.py for GROMACS.
+- [VMD](https://www.ks.uiuc.edu/Research/vmd/) - Large biomolecular systems visualization and analysis using 3D graphics and scripting.
+- [Grace](https://plasma-gate.weizmann.ac.il/Grace/) - 2D plotting tool for Unix-like systems with advanced graphing, fitting, and analysis features.
+- [CPPTRAJ](https://amberhub.chpc.utah.edu/cpptraj/) - Fast, parallelizable trajectory analysis from AMBER.
+- [MDAnalysis](https://www.mdanalysis.org/) - Open-source Python library for analyzing MD simulations.
+- [CABS-flex 3.0](https://lcbio.pl/cabsflex3/) - Web server for rapid simulation of protein and peptide structural flexibility using coarse-grained models.
+- [cmd-viewer](https://github.com/Kopec-Lab/cmd-viewer) - Tool for visualizing and analyzing MD simulation trajectories and structural data.
+
+---
+
+## Synthesis and Retrosynthesis Planning
+- [Spaya](https://spaya.ai/app/search) - AI-driven retrosynthesis engine with route ranking and synthetic feasibility scoring.
+- [AiZynthFinder](https://github.com/MolecularAI/aizynthfinder) - Monte Carlo tree search-based retrosynthesis using trained neural networks.
+- [ASKCOS](https://askcos.mit.edu/) - Synthesis route prediction with ML, developed by MIT.
+- [IBM RoboRXN](https://rxn.res.ibm.com/rxn/robo-rxn/welcome) - Automated reaction prediction using transformer models.
+- [MANIFOLD](https://app.postera.ai/manifold/) - Search engine for synthetically accessible molecules and building blocks.
+- [onepot.ai](https://www.onepot.ai/) - AI-enabled molecular editor and synthesis planning platform with an encrypted structure environment.
+
+---
+
+## Specialized Modalities
+
+### PROTACs and Ternary Complexes
+- [PROTAC-db](http://cadd.zju.edu.cn/protacdb/) - Curated database of PROTAC molecules, targets, and linkers for degrader design.
+- [PROsettaC](https://prosettac.weizmann.ac.il/) - Structure-based modeling of ternary complexes for targeted protein degradation.
+
+### Peptide Design
+- [PepDraw](https://pepdraw.com/) - Peptide visualization with annotated physicochemical properties.
+- [PepSite](http://pepsite2.russelllab.org/) - Predict peptide binding sites on protein surfaces using structural data.
+- [Peptimap](https://peptimap.bu.edu/) - Peptide mapping and binding hotspots identification.
+
+---
+
+## Machine Learning and AI
+
+### Core Libraries
+- [scikit-learn](https://scikit-learn.org/) - General-purpose ML library for classification, regression, clustering, and model evaluation.
+- [PyTorch](https://pytorch.org/) - Deep learning framework with extensive support for neural network modeling.
+- [TensorFlow](https://www.tensorflow.org/) - End-to-end ML platform for scalable model development and deployment.
+- [Keras](https://keras.io/) - High-level neural network API running on top of TensorFlow, designed for fast experimentation.
+- [NumPy](https://numpy.org/) - Core library for numerical computing with support for arrays, matrices, and linear algebra.
+- [Pandas](https://pandas.pydata.org/) - Data manipulation and analysis toolkit built on top of NumPy.
+- [Matplotlib](https://matplotlib.org/) - Comprehensive library for creating static, animated, and interactive visualizations in Python.
+- [Seaborn](https://seaborn.pydata.org/) - Statistical data visualization library built on top of Matplotlib.
+
+### Chemistry-focused ML Frameworks
+- [DeepChem](https://github.com/deepchem/deepchem) - Open-source deep learning framework for chemistry and biology.
+- [scikit-mol](https://github.com/datamol-io/scikit-mol) - Open-source toolkit bridging RDKit and scikit-learn for molecular ML workflows.  
+- [Chemprop](https://github.com/chemprop/chemprop) - Directed message passing neural networks for molecular property prediction.
+- [ChemML](https://github.com/hachmannlab/chemml) - Machine learning and informatics suite for analyzing, mining, and modeling chemical and materials data.
+- [Oloren ChemEngine](https://github.com/Oloren-AI/olorenchemengine) - Unified API for molecular property prediction with uncertainty quantification, interpretability, and model tuning.
+- [TorchDrug](https://torchdrug.ai/) - A machine learning library for drug discovery with support for GNNs and molecular datasets.
+- [DGL-LifeSci](https://github.com/awslabs/dgl-lifesci) - Graph deep learning toolkit for life sciences using the Deep Graph Library.
+- [iChem](https://github.com/mqcomplab/iChem) - Python cheminformatics package for molecular comparisons, fingerprints, and chemical data analysis.
+- [LigandForge](https://github.com/HTS-Oracle/LigandForge) - ML-based structure-guided de novo ligand generation and optimization for hit identification.
+- [LigandForge Web](https://ligandforge.onrender.com/) - Web interface for LigandForge with interactive 3D visualization of lead compound candidates.
+
+### Pretrained Models
+- [MolBERT](https://github.com/BenevolentAI/MolBERT) - Transformer-based molecular representation learning.
+- [ChemBERTa](https://huggingface.co/seyonec/ChemBERTa-zinc-base-v1) - Pretrained BERT-like models for molecules from SMILES.
+- [Uni-Mol](https://github.com/dptech-corp/Uni-Mol) - 3D molecular representation learning framework.
+- [Boltz-2](https://github.com/jwohlwend/boltz) - A foundation model that jointly predicts structure and binding affinity, rivaling physics-based FEP methods in accuracy.
+- [Zatom](https://github.com/Zatom-AI/zatom) - AI-driven generative chemistry platform for discovering and analyzing molecular structures.
+
+### AutoML and Optimization
+- [Auto-sklearn](https://automl.github.io/auto-sklearn/master/) - Automated machine learning for scikit-learn.
+- [TPOT](https://epistasislab.github.io/tpot/) - Genetic programming-based AutoML for optimizing ML pipelines.
+- [Optuna](https://optuna.org/) - Hyperparameter optimization framework for machine learning.
+
+### Molecule Standardization
+- [MolVS](https://github.com/mcs07/MolVS) - Molecule validation and standardization library based on RDKit.
+- [cleanmol](https://github.com/nurtilekgalimov/cleanmol) - Python library for cleaning, standardizing, and preparing molecular structures for cheminformatics workflows.
+
+---
+
+## Utility and Workflow Tools
+- [ProteinsPlus](https://proteins.plus/) - A web-based platform designed to assist life scientists in analyzing and working with protein structures.
+- [OPSIN](https://opsin.ch.cam.ac.uk) - Convert IUPAC names to chemical structures.
+- [OSRA](https://cactus.nci.nih.gov/cgi-bin/osra/index.cgi) - Extract chemical structures from images.
+- [ChemPlot](https://chemplot.streamlit.app/) - Chemical space visualization.
+- [ChemDB](http://cdb.ics.uci.edu/) - Chemoinformatics portal with compound data and tools.
+- [Screening Explorer](http://stats.drugdesign.fr/) - Analyze screening datasets and hit distributions.
+- [LigRMSD](https://ligrmsd.appsbio.utalca.cl/) - Calculate RMSD between ligand poses.
+- [NERDD](https://nerdd.univie.ac.at/) - Curated drug discovery resources.
+- [LigBuilder3](http://www.pkumdl.cn:8080/ligbuilder3/) - De novo ligand design.
+- [ChemMine Tools](https://chemminetools.ucr.edu/) - Web-based cheminformatics toolkit for compound analysis.
+- [MayaChemTools](http://www.mayachemtools.org/index.html) - Perl/Python scripts for cheminformatics.
+- [Click2Drug](https://www.click2drug.org/) - CADD software and databases directory.
+- [Galaxy Europe](https://usegalaxy-eu.github.io/index-cheminformatics.html) - Galaxy instance for cheminformatics.
+- [CADD Vault](https://drugbud-suite.github.io/CADD_Vault/) - CADD resources repository.
+- [BioMoDes](https://abeebyekeen.com/biomodes-biomolecular-structure-prediction/) - Biomolecular structure prediction and modeling tools.
+- [PlayMolecule](https://open.playmolecule.org/landing) - Interactive molecular modeling and simulation platform.
+- [Ertl Molecular](https://ertlmolecular.com/) - Cheminformatics tools for medicinal chemists, including scaffold analysis, ring replacement, and property calculators.
+- [Datagrok](https://datagrok.ai/) - Environment for working with chemical data, covering full-range of tasks from data access to de novo design.
+- [AssayCurveFit](https://assaycurvefit.com/) - Web application for processing dose-response data and generating IC50/EC50 curve fits.
+- [AssayCurveFit (GitHub)](https://github.com/yapici/assaycurvefit) - Source repository for IC50/EC50 calculation from biochemical assays.
+- [biopipelines](https://github.com/locbp-uzh/biopipelines) - Modular Python framework for automated computational protein and ligand engineering workflows on SLURM clusters.
+- [CHEESE](https://cheese.deepmedchem.com/) - AI-driven interactive tool for analyzing chemical spaces and optimizing hit compounds.
+- [chembl_webresource_client](https://github.com/chembl/chembl_webresource_client) - Official Python client library for programmatic access to the ChEMBL database API.
+- [ChemIllusion MCP](https://chemillusion.com/mcp-server) - Model Context Protocol server providing language models with tools for generating and analyzing molecular data.
+- [ComProScanner](https://github.com/slimeslab/ComProScanner) - Pipeline for automated large-scale profiling and screening of chemical compounds against protein targets.
+- [NAMI](https://github.com/mqcomplab/NAMI) - Computational tool for clustering and evaluating differences across molecular datasets.
+- [Neurosnap](https://neurosnap.ai/) - Web platform providing no-code interfaces to bioinformatics and ML tools including AlphaFold.
+- [PyChem-Pro](https://github.com/vijaymasand/PyChem-Pro) - Pure-Python desktop application for molecular visualization, geometry optimization, and cheminformatics.
+- [rdkit-agent](https://github.com/scottmreed/rdkit-agent) - Agent-first cheminformatics CLI powered by RDKit WASM for structure validation and format conversion.
+
+---
+
+## Learning Resources
+
+### Free Courses
+- [TMP Chem Lectures](https://youtube.com/playlist?list=PLm8ZSArAXicIWTHEWgHG5mDr8YbrdcN1K) - Recorded lectures from a leading cheminformatics summer school.
+- [Strasbourg Summer School in Chemoinformatics](https://youtube.com/playlist?list=PLhgURFExPmJsDuHevu5n8y0R41WsXfbnC) - Summer school lectures.
+- [BIGCHEM](https://bigchem.eu/node/63) - Online course on big data applications in chemistry.
+- [Drug Discovery Course](https://www.stereoelectronics.org/webDD/DD_home.html) - Foundations of drug discovery and development.
+- [drugdesign.org](https://www.drugdesign.org/) - Free courses on drug design and cheminformatics.
+- [Cheminformatics OLCC](https://chem.libretexts.org/Courses/Intercollegiate_Courses/Cheminformatics) - Intercollegiate course on cheminformatics theory and coding.
+- [Python For Cheminformatics Docking](https://pdb101.rcsb.org/train/training-events/python4) - Python tutorials for molecular docking via RCSB.
+- [DDA CDD Workshop](https://wcair.dundee.ac.uk/training/training-resources/computational-drug-design/) - Workshop on generative and computational drug design.
+- [MDTutorials](http://www.mdtutorials.com/gmx/) - Step-by-step tutorials for MD simulations using GROMACS.
+- [Computer Aided Drug Design](https://courses.ebo-bio-solution.co.uk/courses/introduction-to-chemoinformatics-and-computational-drug-discovery/lessons/1-computer-aided-drug-design/) - Foundational introduction to chemoinformatics and computational drug design.
+- [Resources for Learning Bioinformatics](https://learnbioinformatics.org/) - Curated collection of tutorials and materials for bioinformatics and computational biology.
+- [Synthesis Workshop](https://synthesis-workshop.com/) - Open-access video podcast on advanced organic synthesis and medicinal chemistry.
+
+### Blogs
+- [Practical Fragments](http://practicalfragments.blogspot.com/) - Insights into fragment-based drug discovery.
+- [Practical Cheminformatics](http://practicalcheminformatics.blogspot.com/) - Tools and tips for cheminformatics workflows.
+- [Neovarsity](https://neovarsity.org/blogs?categories=CHEMINFORMATICS) - Deep-tech blog on cheminformatics and drug discovery applications.
+- [Cheminformania](https://www.cheminformania.com/) - Cheminformatics meets deep learning and molecular modeling.
+- [Daily Dose of Data Science](https://www.blog.dailydoseofds.com/) - Digestible data science tutorials and concepts.
+- [Machine Learning Mastery](https://machinelearningmastery.com/) - Practical ML guides for developers and scientists.
+- [Chem-Workflows](https://chem-workflows.com/index.html) - Jupyter-based chemistry workflows and tutorials.
+- [Structural Bioinformatics](https://proteinstructures.com/) - Guide to structure-based drug design and protein modeling.
+- [McConnellsMedChem](https://mcconnellsmedchem.com/) - Medicinal chemistry insights and commentary.
+- [DrugDiscovery.NET](http://www.drugdiscovery.net/) - AI-powered approaches to drug discovery.
+- [MacinChem](https://macinchem.org/) - Computational chemistry tools for macOS users.
+- [Jeremy Monat](https://bertiewooster.github.io/) - Cheminformatics research and academic resources.
+- [RDKit blog](https://greglandrum.github.io/rdkit-blog/) - A rich collection of tutorials, technical tips, and experimental insights from Greg Landrum.
+- [DeepMedChem](https://www.deepmedchem.com/) - AI-powered insights, tool reviews, and workflows for modern drug discovery.
+
+### Instructional Notebooks
+- [TeachOpenCADD](https://projects.volkamerlab.org/teachopencadd/all_talktorials.html) - Modular Jupyter tutorials for CADD workflows and concepts.
+- [intro_pharma_ai](https://github.com/kochgroup/intro_pharma_ai) - Notebook-based introduction to AI applications in pharma.
+- [Practical Cheminformatics Tutorials](https://github.com/PatWalters/practical_cheminformatics_tutorials) - Hands-on Jupyter tutorials for RDKit, SAR, clustering, generative models, and ML pipelines.
+- [AI/DL for Life Sciences](https://onlinelibrary.wiley.com/doi/10.1002/ardp.202200628) - Interactive notebooks showcasing AI/DL use cases in life sciences.
+- [Fingerprint Generator Tutorial](https://greglandrum.github.io/rdkit-blog/posts/2023-01-18-fingerprint-generator-tutorial.html) - RDKit blog tutorial on generating and manipulating molecular fingerprints.
+- [how-to-train-your-chemeleon](https://github.com/JacksonBurns/how-to-train-your-chemeleon) - Tutorial and framework for training chemical machine learning models.
+- [rdkit-tips-and-tricks](https://github.com/mohamedzaghloul-lab638/rdkit-tips-and-tricks-/tree/main) - Practical snippets and examples for the RDKit cheminformatics toolkit.
+
+## Labs and Research Groups
+
+- [Carlsson Lab](https://www.carlssonlab.org/) - GPCR modeling, receptor-ligand interactions, MD, docking, and AI for drug discovery. (Uppsala University, Sweden)
+- [InSiliChem](https://insilichem.com/) - Computational chemobiology and metalloenzyme design. (Universitat Autònoma de Barcelona, Spain)
+- [LCBC](https://sites.google.com/view/lcbc) - Molecular dynamics, free energy calculations, retrosynthesis using machine learning. (Seoul National University, Korea) 
+- [Angelo Raymond Rossi](https://angeloraymondrossi.github.io/) - High-performance computing for computational chemistry and cheminformatics. (University of Connecticut, USA)
+- [Laboratory of Chemoinformatics](https://complex-matter.unistra.fr/en/research-teams/laboratory-of-chemoinformatics/team/) - QSAR/QSPR, chemical similarity, and virtual screening. (Université de Strasbourg / CNRS, France)
+- [Erastova Lab](https://www.erastova.xyz/) - Molecular modeling of soft matter and biomolecular simulations. (University of Edinburgh, UK)
+- [The Ballester Group](https://ballestergroup.github.io/) - Developing ML/AI methods for structure-based scoring and virtual screening. (Imperial College London, UK)
+- [Meiler Lab](https://meilerlab.org/) - Rosetta software, protein design, and ML-based protein engineering. (Vanderbilt / Leipzig University, USA / Germany)
+- [COMP3D](https://comp3d.univie.ac.at/) - Develops and applies AI methods to design safe, effective pharmaceuticals and agrochemicals. (University of Vienna, Austria) 
+- [Bonvin Lab](https://www.bonvinlab.org/) - Computational structural biology, HADDOCK, and integrative modeling. (Utrecht University, Netherlands)
+- [Volkamer Lab](https://volkamerlab.org/) - Binding site analysis and AI-powered virtual screening. (Saarland University, Germany)
+- [AI Laboratory for Molecular Engineering](https://ailab.bio/) - PROTACs, molecular glues, and ML for chemistry and life sciences. (Chalmers University, Sweden)  
+- [Loschmidt Labs - PEG](https://loschmidt.chemi.muni.cz/peg/) - Protein and enzyme engineering, AI-assisted enzyme design. (Masaryk University, Czechia)
+- [QSAR4U](https://qsar4u.com/index.php) - Cheminformatics tools, QSAR modeling, CReM, and EasyDock. (Palacky University, Czechia)
+- [LBMD](https://www.chem.kuleuven.be/lbmd/index.html) - Computational strategies to understand and engineer biomolecular systems. (KU Leuven, Belgium)
+
+---
Author	SHA1	Message	Date
promptadmin	9fa1c73c31	[upstream-sync] .github/workflows/pr-skill-scan.yml from K-Dense-AI/scientific-agent-skills@9c9bd2e9 [unknown]	2026-06-26 20:34:47 +00:00
promptadmin	1d35474acd	Merge pull request '[Upstream sync] yboulaamane/awesome-drug-discovery (github) — 2 added, 0 modified' (#5 ) from upstream-sync/awesome-drug-discovery-20260626-b8fbd7 into main Reviewed-on: #5	2026-06-26 20:13:47 +00:00
promptadmin	dcfa9ffb3f	Merge pull request '[Upstream sync] Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery (github) — 4 added, 0 modified' (#6 ) from upstream-sync/awesome-bigdata-ai-drugdiscovery-20260626-d1364e into main Reviewed-on: #6	2026-06-26 20:13:27 +00:00
promptadmin	c7e1521144	Merge pull request '[Upstream sync] HICAI-ZJU/Scientific-LLM-Survey (github) — 1 added, 0 modified' (#7 ) from upstream-sync/scientific-llm-survey-20260626-58560c into main Reviewed-on: #7	2026-06-26 20:13:05 +00:00
promptadmin	e65ea51707	Merge pull request '[Upstream sync] PatWalters/resources_2025 (github) — 1 added, 0 modified' (#8 ) from upstream-sync/resources-2025-20260626-fdc9dd into main Reviewed-on: #8	2026-06-26 20:12:37 +00:00
promptadmin	116c107560	[upstream-sync] README.md from PatWalters/resources_2025@fdc9ddb3 [catalogue]	2026-06-26 19:59:44 +00:00
promptadmin	45158eedee	[upstream-sync] README.md from HICAI-ZJU/Scientific-LLM-Survey@58560c16 [catalogue]	2026-06-26 19:59:28 +00:00
promptadmin	1b2521edb6	[upstream-sync] README.md from Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery@d1364e08 [catalogue]	2026-06-26 19:59:11 +00:00
promptadmin	404459b589	[upstream-sync] DrugDiscovery.md from Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery@d1364e08 [catalogue]	2026-06-26 19:59:06 +00:00
promptadmin	63c46f88d0	[upstream-sync] BigData.md from Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery@d1364e08 [catalogue]	2026-06-26 19:59:02 +00:00
promptadmin	0919d699d5	[upstream-sync] AI.md from Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery@d1364e08 [catalogue]	2026-06-26 19:58:58 +00:00
promptadmin	aaee247877	[upstream-sync] README.md from yboulaamane/awesome-drug-discovery@b8fbd716 [catalogue]	2026-06-26 19:58:42 +00:00
promptadmin	4b5b82f34d	[upstream-sync] CONTRIBUTING.md from yboulaamane/awesome-drug-discovery@b8fbd716 [catalogue]	2026-06-26 19:58:37 +00:00
promptadmin	624764a8f5	Merge pull request '[Upstream sync] Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery (github) — 4 added, 0 modified' (#4 ) from upstream-sync/awesome-bigdata-ai-drugdiscovery-20260626-d1364e into main Reviewed-on: #4	2026-06-26 17:44:50 +00:00
promptadmin	83f2f4a59b	[upstream-sync] README.md from Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery@d1364e08 [catalogue]	2026-06-26 17:38:05 +00:00
promptadmin	79fcf3b147	[upstream-sync] DrugDiscovery.md from Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery@d1364e08 [catalogue]	2026-06-26 17:38:02 +00:00
promptadmin	2c05b4f084	[upstream-sync] BigData.md from Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery@d1364e08 [catalogue]	2026-06-26 17:37:56 +00:00
promptadmin	dbf5f51f6e	[upstream-sync] AI.md from Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery@d1364e08 [catalogue]	2026-06-26 17:37:49 +00:00
promptadmin	dbea6deb11	Merge pull request '[Upstream sync] HICAI-ZJU/Scientific-LLM-Survey (github) — 1 added, 0 modified' (#2 ) from upstream-sync/scientific-llm-survey-20260626-58560c into main Reviewed-on: #2	2026-06-26 17:13:57 +00:00
promptadmin	b1134eedcc	Merge pull request '[Upstream sync] PatWalters/resources_2025 (github) — 1 added, 0 modified' (#3 ) from upstream-sync/resources-2025-20260626-fdc9dd into main Reviewed-on: #3	2026-06-26 17:13:30 +00:00
promptadmin	7689540a06	Merge branch 'main' into upstream-sync/resources-2025-20260626-fdc9dd	2026-06-26 17:12:44 +00:00
promptadmin	446fde7223	Merge pull request '[Upstream sync] yboulaamane/awesome-drug-discovery (github) — 2 added, 0 modified' (#1 ) from upstream-sync/awesome-drug-discovery-20260626-b8fbd7 into main Reviewed-on: #1	2026-06-26 17:11:35 +00:00
promptadmin	07912532c4	[upstream-sync] README.md from PatWalters/resources_2025@fdc9ddb3 [catalogue]	2026-06-26 17:10:45 +00:00
promptadmin	d9b1469c34	[upstream-sync] README.md from HICAI-ZJU/Scientific-LLM-Survey@58560c16 [catalogue]	2026-06-26 17:10:32 +00:00
promptadmin	35fefadeff	[upstream-sync] README.md from yboulaamane/awesome-drug-discovery@b8fbd716 [catalogue]	2026-06-26 17:10:15 +00:00
promptadmin	aed6a3594f	[upstream-sync] CONTRIBUTING.md from yboulaamane/awesome-drug-discovery@b8fbd716 [catalogue]	2026-06-26 17:10:05 +00:00
promptadmin	e872c2353d	Add biomarker strategy design prompt	2026-06-10 17:26:52 +00:00
promptadmin	608934c750	Add IND application section drafter prompt	2026-06-10 17:26:50 +00:00
promptadmin	9f996ff42d	Add ADMET liability interpretation prompt	2026-06-10 17:26:48 +00:00
promptadmin	786c7b312f	Add SAR analysis and bioisostere suggestion prompt	2026-06-10 17:26:46 +00:00
promptadmin	811cf0071a	Add molecular docking interpreter prompt	2026-06-10 17:26:45 +00:00
promptadmin	c47422a485	Add drug target validation prompt	2026-06-10 17:26:43 +00:00
promptadmin	1ffe84e5b1	Add README with resource attribution	2026-06-10 17:26:41 +00:00