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"

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

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

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

Code for running RFdiffusion

A Euclidean diffusion model for structure-based drug design.

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

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

Toward High-Accuracy Open-Source Biomolecular Structure Prediction.

Differentiable, Hardware Accelerated, Molecular Dynamics

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

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

This package contains deep learning models and related scripts for RoseTTAFold

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

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

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

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

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.