Cryptic binding pocket discovery from conformational ensembles. Open-source, reproducible, size-robust benchmarks against fpocket, PocketMiner and CryptoBench.
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Updated
Jul 23, 2026 - Python
Cryptic binding pocket discovery from conformational ensembles. Open-source, reproducible, size-robust benchmarks against fpocket, PocketMiner and CryptoBench.
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)
Adds or removes hydrogen atoms to achieve the appropriate molecular protonation state for a user-specified pH range
Python3 translation of AutoDockTools
Codes for our paper "Programming Biomolecular Interactions with All-Atom Generative Model"
Biomolecular simulation trajectory/data analysis.
Parameter/topology editor and molecular simulator
Quantum chemistry program executor and IO standardizer (QCSchema).
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]
A pocket volume analyzer for use in protein modeling.
Predicting protein-ligand binding sites using deep convolutional neural network
The second version of the Kraken taxonomic sequence classification system
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.
Semiempirical Extended Tight-Binding Program Package
Identification of Protein-Ligand Binding Sites using dipolar EPR data
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