Trending repositories for topic drug-discovery
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
P2Rank: Protein-ligand binding site prediction tool based on machine learning. Stand-alone command line program / Java library for predicting ligand binding pockets from protein structure.
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
P2Rank: Protein-ligand binding site prediction tool based on machine learning. Stand-alone command line program / Java library for predicting ligand binding pockets from protein structure.
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Tools developed for running end-to-end chemistry workflows on quantum computers and simulators.
A collection of research papers, datasets and software related to knowledge graphs for drug discovery. Accompanies the paper "A review of biomedical datasets relating to drug discovery: a knowledge gr...
P2Rank: Protein-ligand binding site prediction tool based on machine learning. Stand-alone command line program / Java library for predicting ligand binding pockets from protein structure.
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
Deploy a solution to research on drug discovery problems using quantum computing and classical computing resources.
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
A curated list of resources for machine learning for small-molecule drug discovery
Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval, Nat Mach Intell 2023 (https://www.nature.com/articles/s42256-023-00759-6)
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
Tools developed for running end-to-end chemistry workflows on quantum computers and simulators.
Deploy a solution to research on drug discovery problems using quantum computing and classical computing resources.
A collection of research papers, datasets and software related to knowledge graphs for drug discovery. Accompanies the paper "A review of biomedical datasets relating to drug discovery: a knowledge gr...
P2Rank: Protein-ligand binding site prediction tool based on machine learning. Stand-alone command line program / Java library for predicting ligand binding pockets from protein structure.
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
A curated list of resources for machine learning for small-molecule drug discovery
Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval, Nat Mach Intell 2023 (https://www.nature.com/articles/s42256-023-00759-6)
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Mixed continous/categorical flow-matching model for de novo molecule generation.
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
NequIP is a code for building E(3)-equivariant interatomic potentials
Therapeutics Commons: Artificial Intelligence Foundation for Therapeutic Science
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
A collection of research papers, datasets and software related to knowledge graphs for drug discovery. Accompanies the paper "A review of biomedical datasets relating to drug discovery: a knowledge gr...
Interaction Fingerprints for protein-ligand complexes and more
Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Tools developed for running end-to-end chemistry workflows on quantum computers and simulators.
Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval, Nat Mach Intell 2023 (https://www.nature.com/articles/s42256-023-00759-6)
Mixed continous/categorical flow-matching model for de novo molecule generation.
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.
Tools developed for running end-to-end chemistry workflows on quantum computers and simulators.
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
A collection of research papers, datasets and software related to knowledge graphs for drug discovery. Accompanies the paper "A review of biomedical datasets relating to drug discovery: a knowledge gr...
Deploy a solution to research on drug discovery problems using quantum computing and classical computing resources.
A collection of resources for Deep Learning in Python for Life Sciences (with focus on biotech and pharma).
Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval, Nat Mach Intell 2023 (https://www.nature.com/articles/s42256-023-00759-6)
SELFormer: Molecular Representation Learning via SELFIES Language Models
An interoperable Python framework for biomolecular simulation.
A tool for creating Quantitative Structure Property Relationship (QSPR) models.
Interpretable AI pipeline improving binding predictions for novel protein targets and ligands
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Multi-target de novo molecular generator conditioned on AlphaFold's latent protein embeddings.
Searching for structural similarities across billions of molecules in milliseconds
Mixed continous/categorical flow-matching model for de novo molecule generation.
Awesome list of the data and AI/ML related projects with direct Life Science Companies participation
Official Github for "PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling" (NeurIPS 2023 Workshop)
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
A powerful and flexible machine learning platform for drug discovery
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
[IJCAI 2023 survey track]A curated list of resources for chemical pre-trained models
Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval, Nat Mach Intell 2023 (https://www.nature.com/articles/s42256-023-00759-6)
NequIP is a code for building E(3)-equivariant interatomic potentials
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
Therapeutics Commons: Artificial Intelligence Foundation for Therapeutic Science
Python package for graph neural networks in chemistry and biology
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
DrugGPT: A GPT-based Strategy for Designing Potential Ligands Targeting Specific Proteins
Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval, Nat Mach Intell 2023 (https://www.nature.com/articles/s42256-023-00759-6)
A tool for creating Quantitative Structure Property Relationship (QSPR) models.
Multi-target de novo molecular generator conditioned on AlphaFold's latent protein embeddings.
Mixed continous/categorical flow-matching model for de novo molecule generation.
PyTrial: A Comprehensive Platform for Artificial Intelligence for Drug Development
An interoperable Python framework for biomolecular simulation.
SELFormer: Molecular Representation Learning via SELFIES Language Models
Implementation of ICLR23 paper "Molecule Generation for Target Protein Binding with Structural Motifs"
Interpretable AI pipeline improving binding predictions for novel protein targets and ligands
Awesome list of the data and AI/ML related projects with direct Life Science Companies participation
[ICLR'23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
A PyTorch implementation of a Generative Flow Network (GFlowNet) proposed by Bengio et al. (2021)
HyperPCM: Robust task-conditioned modeling of drug-target interactions