drug-pocket-detection

What problem does it solve?

This Skill identifies and ranks potential ligandable pockets on a protein structure so you know where drug-like molecules are most likely to bind before running docking.

Core Features & Use Cases

• Pocket detection on a single conformer: Generates ranked candidate pockets from either geometry-based fpocket (default) or ML-based P2Rank.
• Per-pocket provenance-rich outputs: Returns geometric center, lining residues, pocket volume (fpocket), and druggability scores (fpocket logistic-regression or P2Rank ligandability probability).
• Docking-free workflow integration: Explicitly does not dock, but prepares the handoff by converting the chosen pocket into a docking-box input via pocket-to-box scripts.
• Useful scenarios: When the user provides a protein but has no binding-site information, explores cryptic/allosteric/orphan pockets, or needs to choose where to dock.

Quick Start

Use the drug-pocket-detection skill to run pocket detection on your prepared receptor PDB and return a ranked pocket list with lining residues and druggability scores.