Dock ligands with AutoDock Vina

AutoDock Vina places each ligand in a binding site of your protein and scores the poses (kcal/mol; lower is stronger predicted binding). One job docks up to 100 ligands.

What you need

• The protein: upload a PDB file as protein, or give an RCSB pdb_id and CogniChem downloads it.
• The ligands: 3D structures in SDF, MOL, MOL2, or PDBQT, as ligand (one) or ligands (a list). If you have SMILES, generate 3D structures first with Molecule Conversion (generate_3d), or use the workflow below, which does it for you.
• Where to dock, one of:
  • pockets: find pockets automatically (with P2Rank) and dock into the top n, or "all";
  • boxes: a table of boxes from Binding-Site Detector;
  • center and size: one box you choose, as [x, y, z] in Å.

Run it

import base64
from pathlib import Path
 
from cognichem_client import CogniChem
 
client = CogniChem.from_env()
 
def file_object(path):
    data = Path(path).read_bytes()
    return {"name": Path(path).name, "content_b64": base64.b64encode(data).decode()}
 
payload = {
    "pdb_id": "XXXX",                       # your target's RCSB id, or "protein": file_object("target.pdb")
    "ligands": [file_object("ligand_1.sdf"), file_object("ligand_2.sdf")],
    "pockets": 1,                           # dock into the top pocket
    "exhaustiveness": 8,
    "num_modes": 9,
}
 
estimate = client.jobs.estimate(job_type="autodockvina", payload=payload)
print(f"Reserves ${estimate.cost:.2f}")
 
job = client.jobs.submit(job_name="vina-screen-1", job_type="autodockvina", payload=payload)
status = client.jobs.wait(job.process_id)
if status.status == "completed":
    client.jobs.result(job.process_id, save_path=".")

Settings

Every field is listed on the AutoDock Vina tool page.

Results

The result has a poses port (PDBQT, one or more poses per ligand, each with binding_affinity_kcal_mol) and the full output as a zip. Treat the scores as a ranking aid: docking scores are rough estimates of binding strength.

Next steps

• From SMILES to docked poses in one run: the Screen a compound library against a target workflow embeds your SMILES in 3D, finds pockets, and docks them.
• Re-score the best poses with GNINA, or re-predict the top hits as complexes with Boltz-2. The virtual screening workflows chain these steps.