# Drug Discovery Pipeline

NOTE: molecule and target inputs and your NGC_API_KEY are transmitted to external...

- Repository: https://github.com/NVIDIA/skills (3812 stars)
- Category: Engineering
- License: Apache-2.0 AND CC-BY-4.0
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add NVIDIA/skills --skill drug-discovery-pipeline -g -a claude-code -y`
Codex: `npx skills add NVIDIA/skills --skill drug-discovery-pipeline -g -a codex -y`
Cursor: `npx skills add NVIDIA/skills --skill drug-discovery-pipeline -g -a cursor -y`
Gemini CLI: `npx skills add NVIDIA/skills --skill drug-discovery-pipeline -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add NVIDIA/skills`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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}]"

The rest: https://github.com/NVIDIA/skills/blob/HEAD/skills/bionemo-drug-discovery-pipeline/SKILL.md