rdk/p2rank
P2Rank: Protein-ligand binding site prediction from protein structure based on machine learning.
P2Rank helps researchers in structural biology and drug discovery quickly identify potential ligand-binding sites on protein structures. You provide a protein structure file (like PDB or CIF), and it outputs a list of predicted binding pockets, their scores, and visualization scripts. This tool is ideal for biochemists, pharmacologists, or computational biologists analyzing protein function or screening for drug targets.
399 stars.
Use this if you need to rapidly predict where small molecules might bind on a given protein structure without complex setup or relying on large template databases.
Not ideal if you require a web-based interface for your predictions or need to perform full molecular docking simulations.
Stars
399
Forks
53
Language
Groovy
License
MIT
Category
Last pushed
Mar 09, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/rdk/p2rank"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
Graylab/DL4Proteins-notebooks
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
Peldom/papers_for_protein_design_using_DL
List of papers about Proteins Design using Deep Learning
llnl/protlib-designer
Integer Linear Programming for Protein Library Design
samsinai/FLEXS
Fitness landscape exploration sandbox for biological sequence design.
LBM-EPFL/CARBonAra
Deep learning framework for protein sequence design from a backbone scaffold that can leverage...