1 changed files with 84 additions and 123 deletions
@@ -2,9 +2,9 @@
 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/69f3b99f/README.md
+upstream_source: https://github.com/yboulaamane/awesome-drug-discovery/blob/0c2c26a2/README.md
-upstream_sha: 69f3b99f
+upstream_sha: 0c2c26a2
-imported_at: 2026-07-16
+imported_at: 2026-11-03
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -12,12 +12,10 @@ 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.
+Computational methods for identifying and developing new drug candidates.
 > 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)
@@ -45,10 +43,8 @@ A meticulously curated resource list focused on computational methods for drug d
  - [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)
@@ -57,8 +53,6 @@ A meticulously curated resource list focused on computational methods for drug d
  - [Instructional Notebooks](#instructional-notebooks)
 - [Labs and Research Groups](#labs-and-research-groups)
 ---
 ## Databases and Chemical Libraries
 ### General Compound Libraries
@@ -83,8 +77,6 @@ A meticulously curated resource list focused on computational methods for drug d
 - [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.
 - [The Natural Products Atlas](https://www.npatlas.org/) - An open-access database for microbial natural products structures and metadata.
 - [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.
@@ -112,8 +104,6 @@ A meticulously curated resource list focused on computational methods for drug d
 - [CovalentInDB (CIDB)](https://cadd.zju.edu.cn/cidb/) - A comprehensive database dedicated to covalent inhibitors, targets, and experimental data.
 - [HSADab](https://github.com/proszxppp/HSADab) - Database of binding thermodynamics, structures, and docking data for human serum albumin.
 ---
 ## Target and Protein Data
 ### Protein Structures
@@ -145,8 +135,6 @@ A meticulously curated resource list focused on computational methods for drug d
 - [RFdiffusion](https://github.com/RosettaCommons/RFdiffusion) - Open-source method for de novo protein design using structure-guided diffusion models.
 - [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.
@@ -164,8 +152,7 @@ A meticulously curated resource list focused on computational methods for drug d
 - [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.
-
+- [OREVID](https://orevid.org/) - Ranks existing drugs for each disease on a biomedical knowledge graph and shows the evidence behind every candidate, with an open repurposing benchmark ([OREVID-bench](https://huggingface.co/datasets/orevid/orevid-bench)).
 ---
 ## Ligand Design and Optimization
@@ -190,6 +177,7 @@ A meticulously curated resource list focused on computational methods for drug d
 - [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.
 - [Morfeus](https://github.com/digital-chemistry-laboratory/morfeus) - Python package for calculating 3D steric and electronic parameters of molecules.
 - [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.
@@ -205,6 +193,7 @@ A meticulously curated resource list focused on computational methods for drug d
 - [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.
 - [ParetoMol](https://paretomol.com/) - Free browser-based multi-objective Pareto analysis of drug-like molecules with RDKit.js properties and optional ADMET-AI predictions.
 ### Fragment-Based Drug Design
@@ -215,8 +204,6 @@ A meticulously curated resource list focused on computational methods for drug d
 - [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.
@@ -237,6 +224,7 @@ A meticulously curated resource list focused on computational methods for drug d
 - [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.
 - [Liganx](https://liganx.com/) - Free, browser-based, mutation-aware docking; docks into wild-type and mutant structures and compares the score difference.
 - [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.
@@ -244,8 +232,6 @@ A meticulously curated resource list focused on computational methods for drug d
 - [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.
 - [posecheck-fast](https://github.com/LigandPro/posecheck-fast) - High-throughput docking pose validation with symmetry-corrected RMSD and lightweight distance and clash filters.
@@ -258,8 +244,6 @@ A meticulously curated resource list focused on computational methods for drug d
 - [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
@@ -290,8 +274,6 @@ A meticulously curated resource list focused on computational methods for drug d
 - [cmd-viewer](https://github.com/Kopec-Lab/cmd-viewer) - Tool for visualizing and analyzing MD simulation trajectories and structural data.
 - [Pharmacon](https://github.com/k-georgiou/pharmacon) - Open-source toolkit for molecular dynamics simulation analysis in medicinal chemistry.
 ---
 ## 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.
@@ -300,8 +282,6 @@ A meticulously curated resource list focused on computational methods for drug d
 - [MANIFOLD](https://postera.ai/) - 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
@@ -313,20 +293,8 @@ A meticulously curated resource list focused on computational methods for drug d
 - [PEP-SiteFinder](https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-SiteFinder/) - Predicts peptide-binding sites on protein structures using drug-like ligand mapping.
 - [PEP-FOLD3](https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3/) - De novo peptide structure prediction framework.
 ---
 ## 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/EBjerrum/scikit-mol) - Open-source toolkit bridging RDKit and scikit-learn for molecular ML workflows.
@@ -348,17 +316,10 @@ A meticulously curated resource list focused on computational methods for drug d
 - [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.
@@ -391,8 +352,6 @@ A meticulously curated resource list focused on computational methods for drug d
 - [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
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 - [AI for Chemistry Course](https://github.com/schwallergroup/ai4chem_course) - Lecture slides, Jupyter notebooks, and exercises for machine learning in chemistry.
 - [CCAS Training Materials](https://ccas.nd.edu/research/training-materials/) - Training resources for computer-assisted synthesis tools, reaction modeling, and machine learning.
 - [ML in Chemistry (CHEM 542)](https://sites.rutgers.edu/sun-lab/teach-chem542/) - Rutgers University course materials covering machine learning applications in chemical sciences.
 - [MLCHEM Tutorial Gallery](https://xuhuihuang.github.io/mlchem/html/examples.html) - Jupyter tutorials on machine learning for chemistry.
 - [Quimioinformática Aplicada al Diseño de Fármacos](https://difacquim.gitbook.io/quimioinformatica) - Spanish handbook on chemoinformatics and drug design.
 - [AiChemist Lectures and Seminars](https://aichemist.eu/lectures) - Lectures on AI applications in chemistry and drug discovery.
 ### Blogs
 - [Practical Fragments](http://practicalfragments.blogspot.com/) - Insights into fragment-based drug discovery.
@@ -445,6 +407,7 @@ A meticulously curated resource list focused on computational methods for drug d
 ## 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)
 - [MODLAB - The Molecular Design Laboratory (ETH Zurich)](https://cadd.ethz.ch/) - Leading academic group developing AI‑driven methods for de novo drug design, reaction prediction, polypharmacology modeling, and closed‑loop molecular discovery.
 - [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)
@@ -460,5 +423,3 @@ A meticulously curated resource list focused on computational methods for drug d
 - [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)
 ---