drug-discovery-pipeline

Files (7)

• BENCHMARK.md
• SKILL.md
• config.yml · evals/
• evals.json · evals/
• egfr.pdb · evals/files/
• skill-card.md
• skill.oms.sig

SKILL.md (first lines)

# Drug Discovery Pipeline

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

```
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.

```python
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:

```python
# 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]

From NVIDIA/skills, Apache-2.0 AND CC-BY-4.0.

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