method-deep-learning

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

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

Code for running RFdiffusion

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

Toward High-Accuracy Open-Source Biomolecular Structure Prediction.

Extensible Surrogate Potential of Ab initio Learned and Optimized by Message-passing Algorithm 🍹https://arxiv.org/abs/2010.01196

A Euclidean diffusion model for structure-based drug design.

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)

MaSIF- Molecular surface interaction fingerprints. Geometric deep learning to decipher patterns in molecular surfaces.

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

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.

Predicting protein-ligand binding sites using deep convolutional neural network

Reaction fingerprints, atlases and classification. Code complementing our Nature Machine Intelligence publication on "Mapping the space of chemical reactions using attention-based neural networks" (http://rdcu.be/cenmd).

NequIP is a code for building E(3)-equivariant interatomic potentials

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

This package contains deep learning models and related scripts for RoseTTAFold