bioclaim 0.7.2

A grounding firewall for biomedical LLMs: verifies biological identifiers and claims against authoritative databases, and flags fabrications.

bioclaim

A grounding firewall for biomedical LLMs. Wrap it around any model's output and it verifies the biological identifiers against authoritative databases — UniProtKB, Ensembl, and EBI's Ontology Lookup Service — flagging the ones that are fabricated.

Large language models constantly emit identifiers that look real but don't exist (GO:9999999, HP:9999999), alongside false gene–function and gene–disease claims. In research and clinical settings that is a documented safety risk. bioclaim is a deterministic, low-latency layer that catches it.

Real-model finding: across three models, 48–68% of specialist biology answers contained a wrong biomedical identifier (Llama-3.1-8B 68%, gpt-oss-120B 60%, Llama-3.3-70B 48%). The dominant error is a real, valid accession pointing at the wrong gene — invisible to existence checks, independently verified. Full study: RESULTS.md.

Benchmark result

On a labeled set of 500 LLM-style answers (394 injected fabrications):

Metric	Score
Precision (flagged that were truly fake)	100.0%
Recall (fabrications caught)	95.7% (377 / 394)
F1	0.978
False accusations	0

Every uncaught fabrication was a network-throttled lookup that safely returned UNVERIFIED — not a single fake was checked and missed. Reproduce with:

python scripts/generate_benchmark.py 500
python scripts/benchmark.py data/benchmark_large.jsonl

How it works

Two layers, both designed to never falsely accuse (if a claim can't be verified it is marked UNVERIFIED, never NOT_FOUND):

1. FORMAT — offline, deterministic. Rejects malformed identifiers.
2. EXISTS — live existence check against the source database. Well-formed but absent identifiers are flagged NOT_FOUND. Rate-limit/network errors are retried with backoff, then degrade to UNVERIFIED.
3. CLAIM (v0.5) — label consistency. A real identifier with a wrong description (GO:0006281 (photosynthesis), actually "DNA repair") is flagged SUPPORTED_LABEL_MISMATCH. Synonym-aware, so paraphrases aren't falsely flagged.
4. ENTITY (v0.6) — correspondence. A real UniProt/Ensembl ID attached to the wrong gene ("the UniProt for TP53 is P38398", actually BRCA1) is flagged SUPPORTED_ENTITY_MISMATCH — the hardest class, invisible to existence checks.

Supported identifier types: GO, HP, MONDO, DOID, CHEBI, Ensembl gene (ENSG), UniProtKB.

Every lookup is cached to disk ($BIOCLAIM_CACHE, else ~/.cache/bioclaim/), so after a warm-up bioclaim runs fast, offline-capable, and immune to rate limits.

Install & use

pip install -e .           # from a clone; PyPI release: pip install bioclaim

One-call API:

from bioclaim import check

result = check("TP53 (P04637) is annotated with GO:9999999 (apoptosis).")
print(result.ok)           # False
for p in result.problems:
    print(p)               # GO:9999999: fabricated (does not exist)

Guard any model call (raises if a fabrication slips through):

from bioclaim import Firewall
guarded = Firewall(raise_on_flag=True).guard(call_my_llm)
answer = guarded(prompt)   # BioclaimFlag raised if the answer cites a fake ID

Command line:

bioclaim "TP53 is P04637 and the fake GO:9999999"
echo "some model output" | bioclaim --entity BRCA1

Project layout

bioclaim/              core package (patterns, live sources, validator)
scripts/               runnable CLIs: demo, batch, benchmark, generate_benchmark
data/                  sample_answers.jsonl (labeled demo set)
tests/                 offline unit tests

Run the pieces (from the repo root):

python scripts/demo.py                     # catch fakes in one example answer
python scripts/benchmark.py                # precision/recall on the sample set
python scripts/batch.py your_answers.jsonl # scan your own AI answers
python -m pytest                           # run tests

Roadmap (land-and-expand)

• v0.1–0.3 Identifier validation across ontologies, genes, and proteins (done)
• v0.4 Local database snapshots — microsecond lookups, fully offline, true 100% recall
• v0.5 Claim verification — relationships, not just IDs (gene–disease, gene–function)
• v0.6 Calibrated confidence per claim
• v1.0 Public leaderboard + LLM-framework integrations

Why this design wins

Generality (all identifier types, one layer) · Deployability (pure standard library, 3-line integration) · Speed (deterministic lookups, not agent loops) · Trust (never a false accusation).

License

MIT — see LICENSE.