method-deep-learning

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

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

Code for running RFdiffusion

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

Toward High-Accuracy Open-Source Biomolecular Structure Prediction.

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

A Euclidean diffusion model for structure-based drug design.

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"

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

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

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.

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]

Predicting protein-ligand binding sites using deep convolutional neural network

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

Message Passing Neural Networks for Molecule Property Prediction