interface-gpu

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

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

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

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

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

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

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

Code for running RFdiffusion

A Euclidean diffusion model for structure-based drug design.

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

Differentiable, Hardware Accelerated, Molecular Dynamics

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

[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

Toward High-Accuracy Open-Source Biomolecular Structure Prediction.

AutoDock for GPUs and other accelerators

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.

Public/backup repository of the GROMACS molecular simulation toolkit. Please do not mine the metadata blindly; we use https://gitlab.com/gromacs/gromacs for code review and issue tracking.

GPU-accelerated protein-ligand docking with automated pocket detection, exploring through multi-pocket conditioning. Official Implementation of PocketVina