Trending repositories for topic drug-discovery
BioNeMo Framework: For building and adapting AI models in drug discovery at scale
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
Therapeutics Commons (TDC-2): Multimodal Foundation for Therapeutic Science
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
Multi-target de novo molecular generator conditioned on AlphaFold's latent protein embeddings.
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
A curated list of resources for machine learning for small-molecule drug discovery
BioNeMo Framework: For building and adapting AI models in drug discovery at scale
Multi-target de novo molecular generator conditioned on AlphaFold's latent protein embeddings.
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
A curated list of resources for machine learning for small-molecule drug discovery
Therapeutics Commons (TDC-2): Multimodal Foundation for Therapeutic Science
NequIP is a code for building E(3)-equivariant interatomic potentials
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
BioNeMo Framework: For building and adapting AI models in drug discovery at scale
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
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
Therapeutics Commons (TDC-2): Multimodal Foundation for Therapeutic Science
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
SELFormer: Molecular Representation Learning via SELFIES Language Models
The Ersilia Model Hub, a repository of AI/ML models for infectious and neglected disease research.
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
Multi-target de novo molecular generator conditioned on AlphaFold's latent protein embeddings.
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
Python package for graph neural networks in chemistry and biology
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
BioNeMo Framework: For building and adapting AI models in drug discovery at scale
SELFormer: Molecular Representation Learning via SELFIES Language Models
Multi-target de novo molecular generator conditioned on AlphaFold's latent protein embeddings.
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
A tool for creating Quantitative Structure Property/Activity Relationship (QSPR/QSAR) models.
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
The Ersilia Model Hub, a repository of AI/ML models for infectious and neglected disease research.
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
NequIP is a code for building E(3)-equivariant interatomic potentials
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
A curated list of resources for machine learning for small-molecule drug discovery
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
BioNeMo Framework: For building and adapting AI models in drug discovery at scale
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
Therapeutics Commons (TDC-2): Multimodal Foundation for Therapeutic Science
NequIP is a code for building E(3)-equivariant interatomic potentials
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
[IJCAI 2023 survey track]A curated list of resources for chemical pre-trained models
Python package for graph neural networks in chemistry and biology
The Ersilia Model Hub, a repository of AI/ML models for infectious and neglected disease research.
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
A powerful and flexible machine learning platform for drug discovery
Official Github for "PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening" (Chemical Science)
An interoperable Python framework for biomolecular simulation.
BioNeMo Framework: For building and adapting AI models in drug discovery at scale
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
Official Github for "PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening" (Chemical Science)
Multi-target de novo molecular generator conditioned on AlphaFold's latent protein embeddings.
Uncover meaningful structures of latent spaces learned by generative models with flows!
DrugHIVE: Structure-based drug design with a deep hierarchical generative model
SELFormer: Molecular Representation Learning via SELFIES Language Models
A tool for creating Quantitative Structure Property/Activity Relationship (QSPR/QSAR) models.
An interoperable Python framework for biomolecular simulation.
This is a curated list of research papers, resources and tools related to using Graph Neural Networks (GNNs) for drug discovery.
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
Mixed continous/categorical flow-matching model for de novo molecule generation.
Pytorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"
All in one Structure-Based Virtual Screening workflow based on the concept of consensus docking.
The Ersilia Model Hub, a repository of AI/ML models for infectious and neglected disease research.
Codes for "Property-Aware Relation Networks for Few-shot Molecular Property Prediction (NeurIPS 2021)".
Multi-target de novo molecular generator conditioned on AlphaFold's latent protein embeddings.
Uncover meaningful structures of latent spaces learned by generative models with flows!
All in one Structure-Based Virtual Screening workflow based on the concept of consensus docking.
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
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
NequIP is a code for building E(3)-equivariant interatomic potentials
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)
BioNeMo Framework: For building and adapting AI models in drug discovery at scale
Therapeutics Commons (TDC-2): Multimodal Foundation for Therapeutic Science
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
A powerful and flexible machine learning platform for drug discovery
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
[IJCAI 2023 survey track]A curated list of resources for chemical pre-trained models
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Python package for graph neural networks in chemistry and biology
Mixed continous/categorical flow-matching model for de novo molecule generation.
Multi-target de novo molecular generator conditioned on AlphaFold's latent protein embeddings.
This is a curated list of research papers, resources and tools related to using Graph Neural Networks (GNNs) for drug discovery.
Official Github for "PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening" (Chemical Science)
A tool for creating Quantitative Structure Property/Activity Relationship (QSPR/QSAR) 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)
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
rDock is a fast and versatile Open Source docking program that can be used to dock small molecules against proteins and nucleic acids. It is designed for High Throughput Virtual Screening (HTVS) campa...
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
Toolkit for open antiviral drug discovery by the ASAP Discovery Consortium
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Implementation of ICML23 paper "Learning Subpocket Prototypes for Generalizable Structure-based Drug Design"
An interoperable Python framework for biomolecular simulation.
SELFormer: Molecular Representation Learning via SELFIES Language Models
DrugGPT: A GPT-based Strategy for Designing Potential Ligands Targeting Specific Proteins