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"

Toward High-Accuracy Open-Source Biomolecular Structure Prediction.

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

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

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

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

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

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).

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.

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