End-To-End Molecular Dynamics (MD) Engine using PyTorch

[NeurIPS2025 Spotlight 🔥 ] Official implementation of "UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection"

Knowledge-Guided Diffusion Model for 3D Ligand-Pharmacophore Mapping

Comprehensive library for fast, GPU accelerated molecular gridding for deep learning workflows

Code for running RFdiffusion

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

Differentiable, Hardware Accelerated, Molecular Dynamics

A Euclidean diffusion model for structure-based drug design.

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

Codes for our paper "Programming Biomolecular Interactions with All-Atom Generative Model"

OpenMM is a toolkit for molecular simulation using high performance GPU code.

Official Github for "PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening" (Chemical Science)

[PNAS 2025] Code of "Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design".

Code for the DISCO model: General Multimodal Protein Design Enables DNA-Encoding of Chemistry

A deep learning framework for molecular docking

Deep Site and Docking Pose (DSDP) is a blind docking strategy accelerated by GPUs, developed by Gao Group. For the site prediction part, several modifications are introduced to PUResNet program. The pose sampling part is similar as AutoDock Vina combined with a number of modifications.

This package contains deep learning models and related scripts for RoseTTAFold

Training and inference code for ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design [ICLR 2025 oral]

Toward High-Accuracy Open-Source Biomolecular Structure Prediction.

AutoDock for GPUs and other accelerators