Drug Discovery Pipeline

Drug Discovery Pipeline

Screen drug candidates end-to-end using three BioNeMo NIMs in sequence:

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Step 1: GenMol    →  Step 2: DiffDock  →  Step 3: Boltz2
(Generate mols)      (Dock to target)      (Predict affinity)

Overview

This pipeline is used for:

• 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:

1. Target protein: PDB file or sequence of the binding target
2. Starting point: de novo (no scaffold) or scaffold decoration (known core)?
3. Scoring: drug-likeness (QED) or lipophilicity (LogP)?
4. 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.

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import requests, json, os
import safe as sf                          # pip install safe-mol
from pathlib import Path

NGC_API_KEY = os.getenv("NGC_API_KEY")
HOSTED = True

if 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:

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# 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 in enumerate(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:

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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
• Boltz2 pIC50: predicted -log10(IC50); >6 = sub-micromolar, >8 = very potent
• P(bind): probability of binary binding; >0.7 = likely binder

Quick reference — skill dependencies