ProteinsCPU3 credits

Pocket Detection — fpocket

Identify druggable binding pockets on protein structures

Detect and rank binding pockets on protein structures using fpocket. Identifies druggable sites based on geometric and physicochemical properties, essential for structure-based drug design.

$0.03

per API call

credits per call

/v1/proteins/pockets

API endpoint

Features

Automated pocket detection from PDB

Druggability scoring

Pocket volume and surface area

Residue composition analysis

Multiple pocket ranking

CPU-based, fast execution

Quick Start

fpocket — Python Examplepython

import requests

API_KEY = "sk-sci-your-key-here"
url = "https://scirouter.ai/v1/proteins/pockets"

response = requests.post(url, json={
    "pdb": open("protein.pdb").read()
}, headers={"Authorization": f"Bearer {API_KEY}"})

pockets = response.json()["pockets"]
for p in pockets[:3]:
    print(f"Pocket {p['rank']}: score={p['druggability']:.2f}, "
          f"volume={p['volume']:.0f} A^3")