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End-To-End Molecular Dynamics (MD) Engine using PyTorch
A Euclidean diffusion model for structure-based drug design.
Code for running RFdiffusion
Codes for our paper "Programming Biomolecular Interactions with All-Atom Generative Model"
Differentiable, Hardware Accelerated, Molecular Dynamics
Official Github for "PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening" (Chemical Science)
Comprehensive library for fast, GPU accelerated molecular gridding for deep learning workflows
Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Knowledge-Guided Diffusion Model for 3D Ligand-Pharmacophore Mapping
[NeurIPS2025 Spotlight 🔥 ] Official implementation of "UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection"
OpenMM is a toolkit for molecular simulation using high performance GPU code.
EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein
Code for the DISCO model: General Multimodal Protein Design Enables DNA-Encoding of Chemistry
[PNAS 2025] Code of "Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design".
Toward High-Accuracy Open-Source Biomolecular Structure Prediction.
AutoDock for GPUs and other accelerators
GPU-accelerated protein-ligand docking with automated pocket detection, exploring through multi-pocket conditioning. Official Implementation of PocketVina
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.
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.
A deep learning framework for molecular docking
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