DockingGPU10 credits

DiffDock — AI Molecular Docking

Diffusion-based molecular docking with confidence-ranked poses

DiffDock uses a diffusion generative model to predict protein-ligand binding poses without predefined search boxes. It generates multiple binding poses ranked by confidence score, outperforming traditional docking methods on many benchmarks.

$0.10

per API call

credits per call

/v1/docking/diffdock

API endpoint

Features

No predefined search box required

Confidence-ranked binding poses

GPU-accelerated inference

Supports PDB protein + SMILES ligand input

Up to 40 poses per prediction

Diffusion-based generative model

Quick Start

DiffDock v1.1 — Python Examplepython

import requests

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

response = requests.post(url, json={
    "protein_pdb": open("target.pdb").read(),
    "ligand_smiles": "CC(=O)Oc1ccccc1C(=O)O",  # Aspirin
    "num_poses": 10
}, headers={"Authorization": f"Bearer {API_KEY}"})

job = response.json()
# Poll for results with job_id...