method-deep-learning

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

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

Code for running RFdiffusion

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

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

Toward High-Accuracy Open-Source Biomolecular Structure Prediction.

A Euclidean diffusion model for structure-based drug design.

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"

Differentiable, Hardware Accelerated, Molecular Dynamics

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

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

IF-SitePred is a method for predicting ligand-binding sites on protein structures. It first generates an embedding for each residue of the protein using the ESM-IF1 (inverse folding) model, then performs point cloud clustering to identify binding site centers.

Prediction of binding residues for metal ions, nucleic acids, and small molecules.

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

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

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).