method-deep-learning

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

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

Code for running RFdiffusion

Toward High-Accuracy Open-Source Biomolecular Structure Prediction.

A Euclidean diffusion model for structure-based drug design.

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

Differentiable, Hardware Accelerated, Molecular Dynamics

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

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

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

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

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

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

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).

Predicting protein-ligand binding sites using deep convolutional neural network

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.

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