Statistics for topic drug-discovery
RepositoryStats tracks 603,442 Github repositories, of these 121 are tagged with the drug-discovery topic. The most common primary language for repositories using this topic is Python (72). Other languages include: Jupyter Notebook (23)
Stargazers over time for topic drug-discovery
Most starred repositories for topic drug-discovery (view more)
Trending repositories for topic drug-discovery (view more)
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
Codebase and CLI for PLAPT: A state-of-the-art protein-ligand binding affinity model for drug discovery
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Codebase and CLI for PLAPT: A state-of-the-art protein-ligand binding affinity model for drug discovery
Official Github for "PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening" (Chemical Science)
Mixed continous/categorical flow-matching model for de novo molecule generation.
PyTrial: A Comprehensive Platform for Artificial Intelligence for Drug Development
This repo contains the codes for our paper Conditional Antibody Design as 3D Equivariant Graph Translation.
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
Official Github for "PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening" (Chemical Science)
[TMLR 2024] Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields
Codebase and CLI for PLAPT: A state-of-the-art protein-ligand binding affinity model for drug discovery
Codes for our paper "Full-Atom Peptide Design with Geometric Latent Diffusion" (NeurIPS 2024)
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
Codebase and CLI for PLAPT: A state-of-the-art protein-ligand binding affinity model for drug discovery
BioNeMo Framework: For building and adapting AI models in drug discovery at scale
Codebase and CLI for PLAPT: A state-of-the-art protein-ligand binding affinity model for drug discovery
[TMLR 2024] Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields
Mixed continous/categorical flow-matching model for de novo molecule generation.
BioNeMo Framework: For building and adapting AI models in drug discovery at scale
Codes for our paper "Full-Atom Peptide Design with Geometric Latent Diffusion" (NeurIPS 2024)
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!
NVIDIA BioNeMo blueprint for generative AI-based virtual screening
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
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
Codebase and CLI for PLAPT: A state-of-the-art protein-ligand binding affinity model for drug discovery
Mixed continous/categorical flow-matching model for de novo molecule generation.
Multi-target de novo molecular generator conditioned on AlphaFold's latent protein embeddings.