Protein Ligand INteraction Dataset and Evaluation Resource

PINDER: The Protein INteraction Dataset and Evaluation Resource

Implementation of the Confidence Bootstrapping procedure for protein-ligand docking.

Code for the paper https://arxiv.org/abs/2402.04997

AlphaFold Meets Flow Matching for Generating Protein Ensembles

Fast protein backbone generation with SE(3) flow matching.

Implementation of Torsional Diffusion for Molecular Conformer Generation (NeurIPS 2022)

Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Sandbox for Deep-Learning based Computational Protein Design

Dir-GNN is a machine learning model that enables learning on directed graphs.

VirtualFlow for Virtual Screening

Implementation of DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models in PyTorch (ICLR 2023 - MLDD Workshop)

EigenFold: Generative Protein Structure Prediction with Diffusion Models

Implementation for SE(3) diffusion model with application to protein backbone generation

Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

Listing of papers about machine learning for proteins.

Code for NeurIPS 2022 Paper, "Poisson Flow Generative Models" (PFGM)

DiffLinker: Equivariant 3D-Conditional Diffusion Model for Molecular Linker Design

Deep Learning for Lung Cancer Risk Prediction using LDCT

Rex Ying's Ph.D. Thesis, Stanford University

Improved diffusion generative models with subspaces

GEOM: Energy-annotated molecular conformations

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

EquiDock: geometric deep learning for fast rigid 3D protein-protein docking

Message Passing Neural Networks for Molecule Property Prediction

Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.