diff --git a/upstream/yboulaamane-awesome-drug-discovery/catalogue/CONTRIBUTING.md b/upstream/yboulaamane-awesome-drug-discovery/catalogue/CONTRIBUTING.md new file mode 100644 index 00000000..96dd3a25 --- /dev/null +++ b/upstream/yboulaamane-awesome-drug-discovery/catalogue/CONTRIBUTING.md @@ -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. diff --git a/upstream/yboulaamane-awesome-drug-discovery/catalogue/README.md b/upstream/yboulaamane-awesome-drug-discovery/catalogue/README.md new file mode 100644 index 00000000..5cb27977 --- /dev/null +++ b/upstream/yboulaamane-awesome-drug-discovery/catalogue/README.md @@ -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) + +---