genmol-nim

Files (13)

• BENCHMARK.md
• SKILL.md
• skillspector-baseline.yml · config/
• config.yml · evals/
• evals.json · evals/
• trigger_evals.json · evals/
• api.md · references/
• examples.md · references/
• parameters.md · references/
• science.md · references/
• validation.md · references/
• skill-card.md
• skill.oms.sig

SKILL.md (first lines)

# GenMol NIM

Generate drug-like molecules with GenMol. Use this guide for first-pass
hosted and local usage; load supplemental files only when needed:

- `references/api.md`: endpoints, schema, Docker flags, response fields.
- `references/science.md`: use cases, strengths, limits, and handoffs.
- `references/parameters.md`: SAFE patterns and tuning effects.
- `references/validation.md`: chemical and artifact checks.
- `references/examples.md`: compact request patterns.

## Choose Mode

Ask only when context is unclear:

> Hosted NVIDIA API or local Docker NIM?

- Hosted: `https://health.api.nvidia.com/v1/biology/nvidia/genmol/generate`
- Local: `http://localhost:8000/generate`

Hosted requests use `Authorization: Bearer $NGC_API_KEY`. For local Docker,
authenticate image pulls with `docker login nvcr.io` using `NGC_API_KEY`
(or `NVIDIA_API_KEY` via the preflight). Pass `-e NGC_API_KEY` into the
container for entitlement checks and first-run model downloads. Local inference
requests use no auth header after readiness, so bind the published port to
loopback with `-p 127.0.0.1:8000:8000`. Warm-cache key-free startup varies by
image version and should not be assumed.

## Local Docker

Use credentials already supplied in the shell environment or injected by a
secret manager. Do not load credential files, print keys, or enable shell tracing.
For local setup answers, include this sequence: env preflight, `docker login`
with `--password-stdin`, `docker run`, readiness loop, then a no-auth localhost
request. Do not invent a cache default or drop the `NVIDIA_API_KEY` fallback.

Before executing local setup, explain that registry authentication sends the key
to the NVIDIA registry at https://nvcr.io and first-run model downloads use
about 20 GB in `LOCAL_NIM_CACHE`.
Execute deployment only when the user requests it; for a setup guide, provide
the commands without running them.

For the exact startup preflight (environment checks, `NVIDIA_API_KEY` fallback,
`--shm-size=2G`, both `--ulimit` flags, `docker login`, and the `docker run`
for `nvcr.io/nim/nvidia/genmol:1.0.1`), copy the command block in
[`references/api.md`](references/api.md) under **Local container startup** verbatim.

GenMol is single-GPU; `NIM_TEST_GPU` defaults to `0`. Wait for readiness:

```bash
until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done
```

## SAFE Input

The API field is named `smiles`, but GenMol expects SAFE notation. Masked
positions use `[*{min-max}]`.

- De novo: `safe_input = "[*{20-30}]"`
- Scaffold decoration: `safe_input = scaffold_to_safe("C1CC(=O)NC1", 10, 15)`
- Motif extension: `safe_input = f"[*{{5-10}}].{motif_safe}.[*{{5-10}}]"`
- Lead optimization: encode the hit, then replace a fragment with `.[*{5-12}]`

Use `safe-mol` for conditioned generation. Simple ring scaffolds may raise
`SAFEFragmentationError`; fall back to the original SMILES plus a SAFE mask.

See the `scaffold_to_safe` helper in
[`references/examples.md`](references/examples.md) under **Scaffold Decoration**.

Wider masks increase diversity; tight masks keep analog size more predictable.

## Request Pattern

```python
import os
import requests

HOSTED = True
url = (
    "https://health.api.nvidia.com/v1/biology/nvidia/genmol/generate"
    if HOSTED else "http://localhost:8000/generate"
)
headers = {"Content-Type": "application/json"}
if HOSTED:
    headers["Authorization"] = f"Bearer {os.getenv('NGC_API_KEY')}"

payload = {
    "smiles": "[*{20-30}]",  # SAFE notation
    "num_molecules": 30,
    "temperature": "1",      # string, not float
    "noise": "1",            # string, not float
    "step_size": 1,
    "scoring": "QED",        # or "LogP"
    "unique": False,
}

response = requests.post(url, headers=headers, json=payload, timeout=180)
response.raise_for_status()
result = response.json()
```

Gotchas:

- `temperature` and `noise` are strings.
- `num_molecules` is 1-1000; invalid/duplicate molecules may be filtered, so
  request extra when the user needs a minimum count.
- `scoring` is `"QED"` for drug-likeness or `"LogP"` for lipophilicity.
- Set `unique=True` for deduplicated analog lists.

## Save And Report Output

Sort molecules by score, print the top ranks, and write a `.smi` file as shown
in [`references/examples.md`](references/examples.md) under **Save Ranked
Results**. For chemical validity, uniqueness, PAINS/alerts, and visualization
with RDKit, read `references/validation.md`.

## Limits And Troubleshooting

- Fewer molecules than requested is expected after filtering.

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

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