AutoDock Vina

Identification of Protein-Ligand Binding Sites using dipolar EPR data

Semiempirical Extended Tight-Binding Program Package

Parameter/topology editor and molecular simulator

Biomolecular simulation trajectory/data analysis.

A deep learning framework for molecular docking

Deep Site and Docking Pose (DSDP) is a blind docking strategy accelerated by GPUs, developed by Gao Group. For the site prediction part, several modifications are introduced to PUResNet program. The pose sampling part is similar as AutoDock Vina combined with a number of modifications.

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

Python3 translation of AutoDockTools

Adds or removes hydrogen atoms to achieve the appropriate molecular protonation state for a user-specified pH range

Message Passing Neural Networks for Molecule Property Prediction

Standalone charge assignment from Espaloma framework.

Open-Source Quantum Chemistry – an electronic structure package in C++ driven by Python

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

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.

The second version of the Kraken taxonomic sequence classification system

A pocket volume analyzer for use in protein modeling.

Predicting protein-ligand binding sites using deep convolutional neural network