entity-complex

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

Predicting protein-ligand binding sites using deep convolutional neural network

A comprehensive macromolecular library

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

pythonic interface to virtual screening software

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

AutoDock Vina

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

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

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.

An open library to work with pharmacophores.

This package contains deep learning models and related scripts for RoseTTAFold

Identification of Protein-Ligand Binding Sites using dipolar EPR data

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

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

Interface for AutoDock, molecule parameterization

Experiments with expanded ensembles to explore chemical space

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

Open source code for AlphaFold 2.

Protein Ligand INteraction Dataset and Evaluation Resource