Predict protein-ligand and catalytic pockets and perform molecular docking of a specific ligand to each predicted pocket.

A comprehensive macromolecular library

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

Predicting protein-ligand binding sites using deep convolutional neural network

Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.

AutoDock Vina

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

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

pythonic interface to virtual screening software

Open source code for AlphaFold 2.

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

Identification of Protein-Ligand Binding Sites using dipolar EPR data

Interface for AutoDock, molecule parameterization

Open-source foundation of the user-sponsored PyMOL molecular visualization system.

MD pharmacophores and virtual screening

PhoreGen: Pharmacophore-Oriented 3D Molecular Generation towards Efficient Feature-Customized Drug Discovery https://www.nature.com/articles/s43588-025-00850-5

Prediction of ligand binding site

Experiments with expanded ensembles to explore chemical space

A versatile workflow for the generation of receptor-based pharmacophore models for virtual screening

A deep learning framework for molecular docking