NOTE: molecule and target inputs and your NGC_API_KEY are transmitted to external NVIDIA-hosted API endpoints on every call. Use local NIM containers for confidential or proprietary data. Run a complete computational drug discovery pipeline using NVIDIA BioNeMo NIMs: generate drug-like molecules with GenMol, dock them to a protein target with DiffDock, then predict binding affinity with Boltz2. Use this skill whenever the user wants to generate and screen small molecule drug candidates, perform hit discovery, optimize leads against a protein target, or do virtual screening combining molecule generation, docking, and affinity prediction. Triggers on: drug discovery pipeline, hit discovery, lead optimization, virtual screening, molecule generation, molecular docking, binding affinity, GenMol, DiffDock, Boltz2, SMILES, SAFE notation, NIM microservice. This is a multi-step pipeline composing three BioNeMo NIMs.
NOTE: molecule and target inputs and your NGC_API_KEY are transmitted to external NVIDIA-hosted API endpoints on every call. Use local NIM containers for confidential or proprietary data. Run a complete computational drug discovery pipeline using NVIDIA BioNeMo NIMs: generate drug-like molecules with GenMol, dock them to a protein target with DiffDock, then predict binding affinity with Boltz2. Use this skill whenever the user wants to generate and screen small molecule drug candidates, perform hit discovery, optimize leads against a protein target, or do virtual screening combining molecule generation, docking, and affinity prediction. Triggers on: drug discovery pipeline, hit discovery, lead optimization, virtual screening, molecule generation, molecular docking, binding affinity, GenMol, DiffDock, Boltz2, SMILES, SAFE notation, NIM microservice. This is a multi-step pipeline composing three BioNeMo NIMs.
license
Apache-2.0 AND CC-BY-4.0
allowed-tools
Bash, Read, Write, AskUserQuestion
Drug Discovery Pipeline
Screen drug candidates end-to-end using three BioNeMo NIMs in sequence:
De novo hit discovery: generate drug-like molecules and screen them against a target
Lead optimization: start from a known scaffold and generate improved analogs, then dock and score
Virtual screening: dock a library of candidates and filter by docking confidence + affinity
Before you start
Confirm with the user:
Target protein: PDB file or sequence of the binding target
Starting point: de novo (no scaffold) or scaffold decoration (known core)?
Scoring: drug-likeness (QED) or lipophilicity (LogP)?
API mode: hosted or local Docker?
For local Docker, do not assume all NIMs are running on localhost:8000 at the
same time. Either run one container at a time and hand files/results between
steps, or start each NIM on a distinct host port and set the per-step URLs.
Step 1: Generate molecules with GenMol
GenMol requires SAFE notation input (not raw SMILES). Use the safe-mol package.
import requests, json, os
import safe as sf # pip install safe-molfrom pathlib import Path
NGC_API_KEY = os.getenv("NGC_API_KEY")
HOSTED = Trueif HOSTED:
genmol_url = "https://health.api.nvidia.com/v1/biology/nvidia/genmol/generate"
headers = {"Content-Type": "application/json",
"Authorization": f"Bearer {NGC_API_KEY}"}
else:
genmol_url = "http://localhost:8000/generate"
headers = {"Content-Type": "application/json"}
# De novo generation (no scaffold):
safe_input = "[*{20-30}]"# Scaffold decoration (known core):# scaffold_smiles = "c1ccccc1"# safe_input = sf.encode(scaffold_smiles) + ".[*{5-10}]"
payload = {
"smiles": safe_input, # field is named 'smiles' but takes SAFE notation"num_molecules": 30, # request more to compensate for post-generation filtering"scoring": "QED", # QED or LogP"unique": True,
"temperature": "1.0", # NOTE: must be string, not float"noise": "1.0", # NOTE: must be string, not float
}
r = requests.post(genmol_url, headers=headers, json=payload)
r.raise_for_status()
molecules = r.json()["molecules"]
molecules_sorted = sorted(molecules, key=lambda x: x["score"], reverse=True)
top_20 = molecules_sorted[:20]
print(f"Generated {len(molecules)} valid molecules (requested 30)")
print("Top 5 by QED score:")
for m in top_20[:5]:
print(f" {m['smiles'][:50]} score={m['score']:.4f}")
Step 2: Dock molecules with DiffDock
Prepare the protein and dock each candidate:
# Load protein (ATOM records only)
receptor_pdb_raw = Path("target.pdb").read_text()
receptor_pdb = "\n".join(line for line in receptor_pdb_raw.splitlines()
if line.startswith("ATOM"))
if HOSTED:
diffdock_url = "https://health.api.nvidia.com/v1/biology/mit/diffdock"else:
diffdock_url = "http://localhost:8000/molecular-docking/diffdock/generate"
docking_results = []
for i, mol inenumerate(top_20):
payload = {
"protein": receptor_pdb,
"ligand": mol["smiles"],
"ligand_file_type": "txt", # "txt" for SMILES input"num_poses": 5,
"time_divisions": 20,
"steps": 18,
"save_trajectory": False,
}
r = requests.post(diffdock_url, headers=headers, json=payload)
r.raise_for_status()
result = r.json()
best_conf = result["position_confidence"][0] # rank 1 pose
best_pose = result["ligand_positions"][0]
docking_results.append({
"smiles": mol["smiles"],
"qed_score": mol["score"],
"docking_confidence": best_conf,
"best_pose_sdf": best_pose,
})
print(f" Mol {i+1:2d}: QED={mol['score']:.3f} docking_conf={best_conf:.4f}")
# Rank by docking confidence
docking_results.sort(key=lambda x: x["docking_confidence"], reverse=True)
print(f"\nTop 3 by docking confidence:")
for d in docking_results[:3]:
print(f" {d['smiles'][:50]} conf={d['docking_confidence']:.4f}")
Step 3: Predict binding affinity with Boltz2
For the top docking candidates, predict structure-based binding affinity:
if HOSTED:
boltz_url = "https://health.api.nvidia.com/v1/biology/mit/boltz2/predict"else:
boltz_url = "http://localhost:8000/biology/mit/boltz2/predict"# Use the target protein sequence (not PDB)
target_sequence = "<YOUR_TARGET_PROTEIN_SEQUENCE>"
affinity_results = []
for d in docking_results[:5]: # score top 5 docking hits
payload = {
"polymers": [
{"id": "A", "molecule_type": "protein", "sequence": target_sequence}
],
"ligands": [
{"id": "L1", "smiles": d["smiles"], "predict_affinity": True}
],
"recycling_steps": 3,
"sampling_steps": 50,
"diffusion_samples": 1,
"output_format": "mmcif",
}
r = requests.post(boltz_url, headers=headers, json=payload)
r.raise_for_status()
result = r.json()
aff = result["affinities"]["L1"]
pic50 = aff["affinity_pic50"][0]
prob_binding = aff["affinity_probability_binary"][0]
affinity_results.append({
**d,
"pic50": pic50,
"probability_binding": prob_binding,
})
print(f" {d['smiles'][:40]} pIC50={pic50:.2f} P(bind)={prob_binding:.3f}")
# Final ranking by pIC50
affinity_results.sort(key=lambda x: x["pic50"], reverse=True)
Interpreting results
GenMol QED score: 0–1; >0.5 is drug-like
DiffDock confidence: higher = more reliable binding pose prediction
