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)

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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

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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.

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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-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}]"