molmim-nim

name	molmim-nim
description	Use this skill for MolMIM, NVIDIA's BioNeMo NIM microservice for small-molecule latent-space generation and optimization. Invoke for MolMIM, molecular embeddings, hidden states, latent decoding, sampling around a seed SMILES, CMA-ES guided molecule generation, QED or plogP optimization, hosted NVIDIA API calls, or local Docker deployment.
license	Apache-2.0 AND CC-BY-4.0
compatibility	requests>=2.28; rdkit
allowed-tools	Bash, Read, Write, AskUserQuestion

MolMIM NIM

Generate, sample, embed, and decode small molecules with MolMIM. Use this SKILL.md for first-pass hosted/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: generation, sampling, and optimization effects.
• references/validation.md: SMILES/property/artifact checks.
• references/examples.md: compact hosted/local request patterns.

Choose Mode

Ask only when context is unclear:

Hosted NVIDIA API or local Docker NIM?

• Hosted generation: https://health.api.nvidia.com/v1/biology/nvidia/molmim/generate
• Local generation: http://localhost:8000/generate
• Local embedding: http://localhost:8000/embedding
• Local hidden state: http://localhost:8000/hidden
• Local decode: http://localhost:8000/decode
• Local sampling: http://localhost:8000/sampling
• Local readiness: http://localhost:8000/v1/health/ready

Mode difference: the hosted API reference exposes /generate; the local container exposes the broader latent-space workflow (/embedding, /hidden, /decode, /sampling, /generate). Do not invent hosted latent endpoints.

Hosted requests use Authorization: Bearer $NGC_API_KEY. Local inference uses no auth header after readiness.

Local Docker

Use shell env first; source repo-root .env only if present. Do not print keys. MolMIM docs use NGC_CLI_API_KEY for the local container; this repo accepts NGC_API_KEY or NVIDIA_API_KEY and maps to NGC_CLI_API_KEY for startup. Mount LOCAL_NIM_CACHE at /home/nvs/.cache/nim.

set -a
[ -f .env ] && . ./.env
set +a

if [ -z "${NGC_API_KEY:-}" ] && [ -n "${NVIDIA_API_KEY:-}" ]; then
  export NGC_API_KEY="$NVIDIA_API_KEY"
fi
if [ -z "${NGC_CLI_API_KEY:-}" ] && [ -n "${NGC_API_KEY:-}" ]; then
  export NGC_CLI_API_KEY="$NGC_API_KEY"
fi
: "${NGC_CLI_API_KEY:?Set NGC_API_KEY, NVIDIA_API_KEY, or NGC_CLI_API_KEY}"
: "${LOCAL_NIM_CACHE:?Set LOCAL_NIM_CACHE}"

echo "$NGC_CLI_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin

export NIM_TEST_GPU="${NIM_TEST_GPU:-0}"
mkdir -p "${LOCAL_NIM_CACHE}"
chmod 777 "${LOCAL_NIM_CACHE}"

docker run --rm -it --name molmim \
  --runtime=nvidia \
  -e CUDA_VISIBLE_DEVICES="${NIM_TEST_GPU}" \
  -e NGC_CLI_API_KEY \
  -v "${LOCAL_NIM_CACHE}:/home/nvs/.cache/nim" \
  -p 8000:8000 \
  nvcr.io/nim/nvidia/molmim:1.0.0

Readiness check:

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

Local embedding smoke test after readiness. Local inference uses no Authorization header:

import requests

seed = "CN1C=NC2=C1C(=O)N(C(=O)N2C)C"
response = requests.post(
    "http://localhost:8000/embedding",
    headers={"Content-Type": "application/json"},
    json={"sequences": [seed]},
    timeout=60,
)
response.raise_for_status()
embedding_data = response.json()
embeddings = embedding_data["embeddings"]
print(f"received {len(embeddings)} embedding vector(s)")

Hosted Generation Pattern

Use hosted /generate for seed-SMILES generation or optimization. Use algorithm: "CMA-ES" for guided property optimization and algorithm: "none" for unguided sampling around the seed.

import os
import requests

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

payload = {
    "smi": "CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
    "algorithm": "CMA-ES",
    "num_molecules": 10,
    "property_name": "QED",
    "minimize": False,
    "min_similarity": 0.4,
    "particles": 8,
    "iterations": 3,
}

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

Generation gotchas:

• Field name is smi, not smiles.
• algorithm is "CMA-ES" or "none".
• property_name is "QED" or "plogP".
• num_molecules is 1-100. iterations is 1-1000. particles is 2-1000.
• min_similarity is 0-1 in the hosted API reference; local docs emphasize common values up to 0.7 for constrained optimization.
• scaled_radius is 0-2 and is mainly used with algorithm: "none" or local /sampling.

Local Latent Workflow

Use local-only endpoints for embedding, hidden-state manipulation, and decode. This is also the surface used by the guided optimization example package. For local latent workflows, state explicitly that the hosted API reference exposes /generate; /embedding, /hidden, /decode, and /sampling are local-only in the current docs.

seed = "CC(Cc1ccc(cc1)C(C(=O)O)C)C"
base = "http://localhost:8000"
headers = {"Content-Type": "application/json"}

embedding = requests.post(
    f"{base}/embedding",
    headers=headers,
    json={"sequences": [seed]},
    timeout=60,
)
embedding.raise_for_status()
embedding_data = embedding.json()
embeddings = embedding_data["embeddings"]
print(f"received {len(embeddings)} embedding vector(s)")

hidden = requests.post(
    f"{base}/hidden",
    headers=headers,
    json={"sequences": [seed]},
    timeout=60,
)
hidden.raise_for_status()
hidden_data = hidden.json()
hiddens = hidden_data["hiddens"]
mask = hidden_data["mask"]

decoded = requests.post(
    f"{base}/decode",
    headers=headers,
    json={"hiddens": hiddens, "mask": mask},
    timeout=60,
)
decoded.raise_for_status()

sampled = requests.post(
    f"{base}/sampling",
    headers=headers,
    json={"sequences": [seed], "num_molecules": 10, "scaled_radius": 0.7},
    timeout=60,
)
sampled.raise_for_status()

Save And Validate Output

Save generated SMILES and validate before using them downstream.

from pathlib import Path
import json

def molmim_smiles(result):
    values = []
    if isinstance(result.get("generated"), list):
        for item in result["generated"]:
            if isinstance(item, str):
                values.append(item)
            elif isinstance(item, list):
                values.extend(x for x in item if isinstance(x, str))
    molecules = result.get("molecules")
    if isinstance(molecules, str):
        molecules = json.loads(molecules)
    if isinstance(molecules, list):
        for item in molecules:
            if isinstance(item, dict) and isinstance(item.get("sample"), str):
                values.append(item["sample"])
    return values

generated = molmim_smiles(result)
if not generated:
    raise RuntimeError(f"MolMIM returned no generated molecules: {result}")

Path("molmim_response.json").write_text(json.dumps(result, indent=2))
Path("molmim_generated.smi").write_text("\n".join(generated) + "\n")
for i, smiles in enumerate(generated, start=1):
    print(i, smiles)

Use RDKit when available to check parseability, uniqueness, simple property ranges, and whether seed similarity constraints are plausible. Generated molecules are candidates, not validated hits; use downstream property, docking, affinity, toxicity, and synthetic-feasibility checks before prioritization.

Troubleshooting

• Hosted 404 on /embedding, /hidden, /decode, or /sampling: those endpoints are local-only in the docs.
• 401: missing or unauthorized NGC key for hosted requests.
• Hosted response parsing: live hosted /generate may return molecules as a JSON string of {sample, score} objects, while local endpoints may return generated; parse both.
• 422: invalid SMILES, unsupported algorithm, invalid property_name, or parameter outside documented ranges.
• Local startup auth: set NGC_CLI_API_KEY, or set NGC_API_KEY/NVIDIA_API_KEY and map it as shown above.
• Local startup cache misses: mount LOCAL_NIM_CACHE to /home/nvs/.cache/nim, not /opt/nim/.cache.