P2Rank: Protein-ligand binding site prediction from protein structure based on machine learning.
GPU-accelerated protein-ligand docking with automated pocket detection, exploring through multi-pocket conditioning. Official Implementation of PocketVina
The central entry point to the P2Rank project with links to the individual projects, including references to documentation, datasets, etc.
This repository provides an automated docking solution for ligands and receptor proteins using AutoDock Vina and P2Rank, enhanced with a Streamlit-based web interface for simple and intuitive browser-based operation. It supports high-throughput workflows and includes SLURM integration for advanced task management while remaining easy to use locally
Simple pipeline to execute molecular docking experiments
Repository which incorporates the p2rank software in scipion
Data generation and preprocessing pipeline for PockNet protein binding-site prediction experiments.
Container image for P2Rank, machine learning based prediction of ligand binding sites from protein structure
This repository contains an end-to-end, fully GUI/web-based dual-receptor molecular docking and ADMET profiling pipeline of four cyanobacterial natural products — scytonemin, phycocyanobilin, lyngbyabellin A and nostocarboline — against the BRCA2 DNA-binding domain using PyMOL, AutoDock Vina, PLIP, SwissADME, and ADMETlab 3.0.
All-in-one web platform for protein pocket prediction, ligand docking, ADMET profiling, and MM-GBSA rescoring — powered by P2Rank, AutoDock Vina, RDKit, and OpenMM.
Automated pocket-guided molecular docking and protein–ligand interaction analysis pipeline using AutoDock Vina, P2Rank, Meeko, and ProLIF.
Automated end-to-end molecular docking pipeline for protein and ligands - ADMET screening, P2Rank site prediction, AutoDock Vina docking with compatible output
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