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

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

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.

Official repository for the Boltz biomolecular interaction models

Code for the ProteinMPNN paper

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

fpocket is a very fast open source protein pocket detection algorithm based on Voronoi tessellation. The platform is suited for the scientific community willing to develop new scoring functions and extract pocket descriptors on a large scale level. fpocket is distributed as free open source software.

Burrow-Wheeler Aligner for short-read alignment (see minimap2 for long-read alignment)

TeachOpenCADD: a teaching platform for computer-aided drug design (CADD) using open source packages and data

A Euclidean diffusion model for structure-based drug design.

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

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]

Prediction of binding residues for metal ions, nucleic acids, and small molecules.

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.

The second version of the Kraken taxonomic sequence classification system