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

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.

A comprehensive macromolecular library

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

AutoDock Vina

Predicting protein-ligand binding sites using deep convolutional neural network

pythonic interface to virtual screening software

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

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

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

Identification of Protein-Ligand Binding Sites using dipolar EPR data

Library for computing dynamic non-covalent contact networks in proteins throughout MD Simulation

Experiments with expanded ensembles to explore chemical space

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

MD pharmacophores and virtual screening

Interface for AutoDock, molecule parameterization

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

📐 Symmetry-corrected RMSD in Python

A Euclidean diffusion model for structure-based drug design.

This package contains deep learning models and related scripts for RoseTTAFold