[Upstream sync] yboulaamane/awesome-drug-discovery (github) — 0 added, 1 modified #32
@@ -2,9 +2,9 @@
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title: "Awesome Drug Discovery [](https://awesome.re)"
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task: ""
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lineage_type: import
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upstream_source: https://github.com/yboulaamane/awesome-drug-discovery/blob/1b8ee074/README.md
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upstream_sha: 1b8ee074
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imported_at: 2026-07-16
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upstream_source: https://github.com/yboulaamane/awesome-drug-discovery/blob/4815deff/README.md
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upstream_sha: 4815deff
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imported_at: 2026-08-08
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prompt_class: catalogue
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upstream_changes: accepted
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author: upstream
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@@ -12,62 +12,56 @@ validated: false
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---
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# Awesome Drug Discovery [](https://awesome.re)
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A meticulously curated resource list focused on computational methods for drug discovery.
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Computational methods for identifying and developing new drug candidates.
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> 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)
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---
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## Contents
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- [Databases and Chemical Libraries](#databases-and-chemical-libraries)
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- [General Compound Libraries](#general-compound-libraries)
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- [Natural Product Libraries](#natural-product-libraries)
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- [Bioactivity Databases](#bioactivity-databases)
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- [Target and Protein Data](#target-and-protein-data)
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- [Protein Structures](#protein-structures)
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- [Binding Site and Pocket Detection](#binding-site-and-pocket-detection)
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- [Databases and Chemical Libraries](#databases-and-chemical-libraries)
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||||
- [General Compound Libraries](#general-compound-libraries)
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||||
- [Natural Product Libraries](#natural-product-libraries)
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||||
- [Bioactivity Databases](#bioactivity-databases)
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||||
- [Target and Protein Data](#target-and-protein-data)
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- [Protein Structures](#protein-structures)
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- [Binding Site and Pocket Detection](#binding-site-and-pocket-detection)
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- [Protein Engineering and Modeling](#protein-engineering-and-modeling)
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- [Network Pharmacology](#network-pharmacology)
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- [Ligand Design and Optimization](#ligand-design-and-optimization)
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||||
- [Pharmacophore Modeling](#pharmacophore-modeling)
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- [QSAR and Descriptor Tools](#qsar-and-descriptor-tools)
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- [Network Pharmacology](#network-pharmacology)
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||||
- [Ligand Design and Optimization](#ligand-design-and-optimization)
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||||
- [Pharmacophore Modeling](#pharmacophore-modeling)
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- [QSAR and Descriptor Tools](#qsar-and-descriptor-tools)
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||||
- [Descriptor and Featurization Tools](#descriptor-and-featurization-tools)
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||||
- [Molecular Property Prediction](#molecular-property-prediction)
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||||
- [Fragment-Based Drug Design](#fragment-based-drug-design)
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||||
- [Virtual Screening and Docking](#virtual-screening-and-docking)
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||||
- [Interaction Analysis and Visualization](#interaction-analysis-and-visualization)
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- [Molecular Dynamics and Simulation](#molecular-dynamics-and-simulation)
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- [Engines](#engines)
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- [Topology and Force Field Tools](#topology-and-force-field-tools)
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||||
- [Analysis Tools](#analysis-tools)
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- [Synthesis and Retrosynthesis Planning](#synthesis-and-retrosynthesis-planning)
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- [Specialized Modalities](#specialized-modalities)
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||||
- [PROTACs and Ternary Complexes](#protacs-and-ternary-complexes)
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- [Peptide Design](#peptide-design)
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||||
- [Machine Learning and AI](#machine-learning-and-ai)
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||||
- [Core Libraries](#core-libraries)
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- [Chemistry-focused ML Frameworks](#chemistry-focused-ml-frameworks)
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- [Pretrained Models](#pretrained-models)
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||||
- [AutoML and Optimization](#automl-and-optimization)
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||||
- [Molecule Standardization](#molecule-standardization)
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- [Utility and Workflow Tools](#utility-and-workflow-tools)
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||||
- [Learning Resources](#learning-resources)
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||||
- [Free Courses](#free-courses)
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||||
- [Blogs](#blogs)
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||||
- [Molecular Property Prediction](#molecular-property-prediction)
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||||
- [Fragment-Based Drug Design](#fragment-based-drug-design)
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- [Virtual Screening and Docking](#virtual-screening-and-docking)
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- [Interaction Analysis and Visualization](#interaction-analysis-and-visualization)
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- [Molecular Dynamics and Simulation](#molecular-dynamics-and-simulation)
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||||
- [Engines](#engines)
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||||
- [Topology and Force Field Tools](#topology-and-force-field-tools)
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||||
- [Analysis Tools](#analysis-tools)
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||||
- [Synthesis and Retrosynthesis Planning](#synthesis-and-retrosynthesis-planning)
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||||
- [Specialized Modalities](#specialized-modalities)
|
||||
- [PROTACs and Ternary Complexes](#protacs-and-ternary-complexes)
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- [Peptide Design](#peptide-design)
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- [Machine Learning and AI](#machine-learning-and-ai)
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||||
- [Chemistry-focused ML Frameworks](#chemistry-focused-ml-frameworks)
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||||
- [Pretrained Models](#pretrained-models)
|
||||
- [Molecule Standardization](#molecule-standardization)
|
||||
- [Utility and Workflow Tools](#utility-and-workflow-tools)
|
||||
- [Learning Resources](#learning-resources)
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||||
- [Free Courses](#free-courses)
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- [Blogs](#blogs)
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- [Instructional Notebooks](#instructional-notebooks)
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- [Labs and Research Groups](#labs-and-research-groups)
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---
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## Databases and Chemical Libraries
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### General Compound Libraries
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- [DrugBank](https://go.drugbank.com/) - Comprehensive data on approved and investigational drugs.
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- [ZINC](https://zinc.docking.org/) - Free compounds for screening.
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- [ChemSpider](http://www.chemspider.com/) - Chemical structures and data.
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- [DrugSpaceX](https://drugspacex.simm.ac.cn/) - Chemical and biological spaces.
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- [Mcule](https://mcule.com/) - Virtual screening platform with purchasable compounds.
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- [Otava Chemicals](https://www.otavachemicals.com/) - Screening compounds and building blocks.
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- [ZINC](https://zinc.docking.org/) - Free compounds for screening.
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- [ChemSpider](http://www.chemspider.com/) - Chemical structures and data.
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- [DrugSpaceX](https://drugspacex.simm.ac.cn/) - Chemical and biological spaces.
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- [Mcule](https://mcule.com/) - Virtual screening platform with purchasable compounds.
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- [Otava Chemicals](https://www.otavachemicals.com/) - Screening compounds and building blocks.
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- [Vitas-M Laboratory](https://vitasmlab.biz/) - Chemical libraries for HTS and lead discovery.
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- [Eximed](https://eximedlab.com/Screening-Compounds.html) - 60k+ compounds for virtual screening.
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- [OTAVA NP-like Library](https://otavachemicals.com/sdf) - Screening compounds for prompt delivery.
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@@ -75,34 +69,32 @@ A meticulously curated resource list focused on computational methods for drug d
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- [VAST Chemical Space](https://www.aifchem.com/vast) - 4.6 billion synthetically accessible compounds for virtual screening and hit expansion.
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### Natural Product Libraries
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- [ZINC15 Natural Products](https://zinc15.docking.org/substances/subsets/natural-products/) - 200k+ natural compounds.
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- [COCONUT](https://coconut.naturalproducts.net/) - 400k+ natural products.
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- [LOTUS](https://lotus.naturalproducts.net/) - Annotated molecular data with sourcing organisms.
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- [NPASS](http://bidd.group/NPASS/index.php) - 94k activity-species links.
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- [ANPDB](https://phabidb.vm.uni-freiburg.de/anpdb/) - 27k+ African medicinal plant compounds.
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- [SANCDB](https://sancdb.rubi.ru.ac.za/) - Natural compounds from the plant and marine life in and around South Africa.
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- [CMNPD](https://www.cmnpd.org/) - 31k+ marine natural products.
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- [The Natural Products Atlas](https://www.npatlas.org/) - An open-access database for microbial natural products structures and metadata.
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- [CoumarinDB](https://yboulaamane.github.io/CoumarinDB/) - A manually curated database on coumarins from plants.
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- [ArtemisiaDB](https://yboulaamane.github.io/ArtemisiaDB/) - Artemisia genus compounds.
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- [BIAdb](https://webs.iiitd.edu.in/raghava/biadb/type.php?tp=natural) - A database for benzylisoquinoline alkaloids.
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- [IMPPAT](https://cb.imsc.res.in/imppat/home) - Phytochemicals from Indian medicinal plants.
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- [NP-MRD](https://np-mrd.org/natural_products) - 280k+ NMR-based NP studies.
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- [IBS Natural Compounds](https://www.ibscreen.com/natural-compounds) - 60k+ compounds.
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- [PhytoHub](https://phytohub.eu/) - Dietary phytochemicals and metabolites.
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- [Dr. Duke's Phytochemical DB](https://phytochem.nal.usda.gov/) - Plant compounds and uses.
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- [CyanoMetDB](https://zenodo.org/records/13854577) - Over 3,000 cyanobacterial metabolites.
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- [Seaweed Metabolite DB](https://www.swmd.co.in/) - Marine algae compounds.
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- [FooDB](https://foodb.ca/) - A comprehensive resource on food constituents.
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- [ZINC15 Natural Products](https://zinc15.docking.org/substances/subsets/natural-products/) - 200k+ natural compounds.
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- [COCONUT](https://coconut.naturalproducts.net/) - 400k+ natural products.
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- [LOTUS](https://lotus.naturalproducts.net/) - Annotated molecular data with sourcing organisms.
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- [NPASS](http://bidd.group/NPASS/index.php) - 94k activity-species links.
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- [ANPDB](https://phabidb.vm.uni-freiburg.de/anpdb/) - 27k+ African medicinal plant compounds.
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- [SANCDB](https://sancdb.rubi.ru.ac.za/) - Natural compounds from the plant and marine life in and around South Africa.
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- [CMNPD](https://www.cmnpd.org/) - 31k+ marine natural products.
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- [The Natural Products Atlas](https://www.npatlas.org/) - An open-access database for microbial natural products structures and metadata.
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- [BIAdb](https://webs.iiitd.edu.in/raghava/biadb/type.php?tp=natural) - A database for benzylisoquinoline alkaloids.
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- [IMPPAT](https://cb.imsc.res.in/imppat/home) - Phytochemicals from Indian medicinal plants.
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- [NP-MRD](https://np-mrd.org/natural_products) - 280k+ NMR-based NP studies.
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- [IBS Natural Compounds](https://www.ibscreen.com/natural-compounds) - 60k+ compounds.
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- [PhytoHub](https://phytohub.eu/) - Dietary phytochemicals and metabolites.
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- [Dr. Duke's Phytochemical DB](https://phytochem.nal.usda.gov/) - Plant compounds and uses.
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- [CyanoMetDB](https://zenodo.org/records/13854577) - Over 3,000 cyanobacterial metabolites.
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||||
- [Seaweed Metabolite DB](https://www.swmd.co.in/) - Marine algae compounds.
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- [FooDB](https://foodb.ca/) - A comprehensive resource on food constituents.
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### Bioactivity Databases
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- [ChEMBL](https://www.ebi.ac.uk/chembl/) - Bioactivity and ADMET data.
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- [SureChEMBL](https://www.surechembl.org/) - Patent chemistry search.
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- [BindingDB](https://www.bindingdb.org/) - Binding affinities for biomolecules.
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- [PubChem](https://pubchem.ncbi.nlm.nih.gov/) - Structures, properties, and bioassays.
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- [PDBbind](http://www.pdbbind.org.cn/index.php) - Protein-ligand affinity data.
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- [BRENDA](https://www.brenda-enzymes.org/) - Enzyme properties and functions.
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- [ExCAPE-DB](https://solr.ideaconsult.net/search/excape/) - A large-scale chemogenomics database.
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- [ChEMBL](https://www.ebi.ac.uk/chembl/) - Bioactivity and ADMET data.
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- [SureChEMBL](https://www.surechembl.org/) - Patent chemistry search.
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- [BindingDB](https://www.bindingdb.org/) - Binding affinities for biomolecules.
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- [PubChem](https://pubchem.ncbi.nlm.nih.gov/) - Structures, properties, and bioassays.
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- [PDBbind](http://www.pdbbind.org.cn/index.php) - Protein-ligand affinity data.
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- [BRENDA](https://www.brenda-enzymes.org/) - Enzyme properties and functions.
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- [ExCAPE-DB](https://solr.ideaconsult.net/search/excape/) - A large-scale chemogenomics database.
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- [Therapeutics Data Commons](https://tdcommons.ai/) - AI/ML-ready datasets and learning tasks for therapeutics.
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- [Therapeutic Target Database (TTD)](https://idrblab.net/ttd/) - Drug targets with linked diseases and compounds.
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- [Aircheck Datasets](https://aircheck.ai/datasets) - Curated DEL datasets for AI‑driven drug discovery, enabling benchmarking and model development.
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@@ -112,8 +104,6 @@ A meticulously curated resource list focused on computational methods for drug d
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- [CovalentInDB (CIDB)](https://cadd.zju.edu.cn/cidb/) - A comprehensive database dedicated to covalent inhibitors, targets, and experimental data.
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- [HSADab](https://github.com/proszxppp/HSADab) - Database of binding thermodynamics, structures, and docking data for human serum albumin.
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---
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## Target and Protein Data
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### Protein Structures
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@@ -145,8 +135,6 @@ A meticulously curated resource list focused on computational methods for drug d
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- [RFdiffusion](https://github.com/RosettaCommons/RFdiffusion) - Open-source method for de novo protein design using structure-guided diffusion models.
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- [Melodia](https://github.com/rwmontalvao/Melodia_py) - Python library for analyzing and comparing protein structure shapes via differential geometry.
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---
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## Network Pharmacology
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- [GeneCards](https://www.genecards.org/) - Human gene database with genomic, proteomic, and clinical data.
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- [SwissTargetPrediction](http://www.swisstargetprediction.ch/) - Predicts targets of small molecules via similarity-based screening.
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@@ -165,8 +153,6 @@ A meticulously curated resource list focused on computational methods for drug d
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- [Polypharmacology Browser PPB3](https://ppb3.gdb.tools/) - Deep learning tool predicting off-target effects and polypharmacology for bioactive molecules.
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- [Drug-Target Interaction Explorer](https://github.com/yashhhhhhhhh504/Drug-Target-Interaction-Explorer) - Dashboard for exploring and visualizing drug-target interaction networks.
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---
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## Ligand Design and Optimization
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### Pharmacophore Modeling
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@@ -184,11 +170,11 @@ A meticulously curated resource list focused on computational methods for drug d
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- [pyADA](https://github.com/jeffrichardchemistry/pyADA) - Assesses the applicability domain of molecular fingerprints via similarity-based thresholds for QSAR validation.
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### Descriptor and Featurization Tools
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- [RDKit](https://www.rdkit.org/) - Open-source cheminformatics toolkit with descriptor, fingerprint, and molecular manipulation support.
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- [PaDEL-Descriptor](http://www.yapcwsoft.com/dd/padeldescriptor/) - Java tool for calculating molecular descriptors and fingerprints.
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- [Mordred](https://github.com/mordred-descriptor/mordred) - Python library with 1800+ molecular descriptors.
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- [CDK](https://cdk.github.io/) - Java cheminformatics library with descriptor calculators.
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- [alvaDesc](https://www.alvascience.com/alvadesc/) - Commercial software for molecular descriptors and fingerprints.
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- [RDKit](https://www.rdkit.org/) - Open-source cheminformatics toolkit with descriptor, fingerprint, and molecular manipulation support.
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- [PaDEL-Descriptor](http://www.yapcwsoft.com/dd/padeldescriptor/) - Java tool for calculating molecular descriptors and fingerprints.
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- [Mordred](https://github.com/mordred-descriptor/mordred) - Python library with 1800+ molecular descriptors.
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- [CDK](https://cdk.github.io/) - Java cheminformatics library with descriptor calculators.
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- [alvaDesc](https://www.alvascience.com/alvadesc/) - Commercial software for molecular descriptors and fingerprints.
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- [MolFeat](https://molfeat.datamol.io/) - Python package for molecular featurization and embeddings.
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- [Dragon](https://www.talete.mi.it/products/dragon_description.htm) - Commercial molecular descriptor calculator (widely cited).
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- [ChemDescriptor](https://github.com/darkreactions/chemdescriptor) - Open-source tool for generating chemical descriptors and fingerprints, supporting cheminformatics workflows.
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@@ -210,13 +196,11 @@ A meticulously curated resource list focused on computational methods for drug d
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- [SwissSidechain](https://www.swisssidechain.ch/) - Fragment and linker library for small molecule design.
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- [BoBER](http://bober.insilab.org/) - Bioisosteric replacements for lead optimization.
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- [FragBuilder](https://github.com/andersx/fragbuilder) - Python API for building peptide-like and small molecule fragments.
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- [FragBuilder](https://github.com/andersx/fragbuilder) - Python API for building peptide-like and small molecule fragments.
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- [SeeSAR](https://www.biosolveit.de/SeeSAR/) - Fragment growing and linking software (free academic version).
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- [Enamine Fragment Libraries](https://enamine.net/compound-libraries/fragment-libraries) - Large curated collection of diverse fragments for FBDD.
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- [FragmentFinder](https://github.com/1JELC1/FragmentFinder) - Computational tool for identifying and matching structural fragments in drug discovery workflows.
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---
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## Virtual Screening and Docking
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- [OpenBabel](https://openbabel.org/index.html) - Format conversion and ligand prep.
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- [Meeko](https://github.com/forlilab/Meeko) - Prepares ligands/receptors for AutoDock by assigning partial charges and atom types.
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@@ -225,8 +209,8 @@ A meticulously curated resource list focused on computational methods for drug d
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- [AutoDockTools](https://autodocksuite.scripps.edu/adt/) - AutoDock GUI.
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- [AutoDock Vina](https://vina.scripps.edu/) - Popular docking software.
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- [AutoDock-GPU](https://github.com/ccsb-scripps/AutoDock-GPU) - GPU-accelerated version of AutoDock for faster ligand-receptor docking.
|
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- [DiffDock](https://github.com/gcorso/DiffDock) - Deep learning-based docking tool that predicts ligand poses directly from protein structures using diffusion models.
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- [EasyDockVina2](https://github.com/S3cr3t-SDN/EasyDockVina2) - Vina automation.
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||||
- [DiffDock](https://github.com/gcorso/DiffDock) - Deep learning-based docking tool that predicts ligand poses directly from protein structures using diffusion models.
|
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- [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.
|
||||
@@ -244,8 +228,6 @@ 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 +240,6 @@ 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.
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||||
---
|
||||
|
||||
## Molecular Dynamics and Simulation
|
||||
|
||||
### Engines
|
||||
@@ -290,8 +270,6 @@ 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 +278,6 @@ 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
|
||||
@@ -313,23 +289,11 @@ 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.
|
||||
- [scikit-mol](https://github.com/EBjerrum/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://pypi.org/project/olorenchemengine/) - Unified API for molecular property prediction with uncertainty quantification, interpretability, and model tuning.
|
||||
@@ -348,17 +312,10 @@ 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 +348,6 @@ 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
|
||||
@@ -408,6 +363,10 @@ A meticulously curated resource list focused on computational methods for drug d
|
||||
- [MDTutorials](http://www.mdtutorials.com/gmx/) - Step-by-step tutorials for MD simulations using GROMACS.
|
||||
- [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.
|
||||
- [AI for Chemistry (ai4chem Book)](https://zzhenglab.github.io/ai4chem/intro.html) - An open-access book and interactive guide to machine learning and AI in chemistry.
|
||||
- [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.
|
||||
|
||||
### Blogs
|
||||
- [Practical Fragments](http://practicalfragments.blogspot.com/) - Insights into fragment-based drug discovery.
|
||||
@@ -424,8 +383,12 @@ A meticulously curated resource list focused on computational methods for drug d
|
||||
- [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.
|
||||
- [The Data Chemist's Handbook](https://data-chemist-handbook.github.io/) - A curated handbook with guidelines, code snippets, and tools for data-driven chemistry.
|
||||
- [Awesome Learning Digital Chemistry](https://github.com/mlederbauer/awesome-learning-digital-chemistry) - Curated compilation of resources for learning digital chemistry, including courses, tutorials, and books.
|
||||
- [CCAS Data sets, Tools, and Workflows](https://ccas.nd.edu/research/data-sets-tools-and-workflows/) - A repository of datasets and computational workflows for computer-assisted organic synthesis.
|
||||
|
||||
### Instructional Notebooks
|
||||
- [DeepChem Tutorials](https://github.com/deepchem/deepchem/tree/master/examples/tutorials) - Comprehensive set of tutorials covering deep learning for chemistry, biology, and materials science.
|
||||
- [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.
|
||||
@@ -438,18 +401,17 @@ A meticulously curated resource list focused on computational methods for drug d
|
||||
|
||||
- [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)
|
||||
- [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)
|
||||
- [COMP3D](https://comp3d.univie.ac.at/) - Develops and applies AI methods to design safe, effective pharmaceuticals and agrochemicals. (University of Vienna, Austria)
|
||||
- [Dral Group](http://dr-dral.com/) - AI-enhanced computational chemistry, quantum chemical methods, and development of MLatom. (Xiamen University, China)
|
||||
- [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)
|
||||
- [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)
|
||||
|
||||
---
|
||||
|
||||
Reference in New Issue
Block a user