tox-antitargets-mcp-server

An MCP server that reproduces the results of:

Nikitin, I.; Morgunov, I.; Safronov, V.; Kalyuzhnaya, A.; Fedorov, M. Towards Explainable Computational Toxicology: Linking Antitargets to Rodent Acute Toxicity. Pharmaceutics 2025, 17, 1573. https://doi.org/10.3390/pharmaceutics17121573

It exposes every figure, statistic and finding of the paper as MCP tools, computed deterministically from the openly published dataset (chemagents/ld50-antitargets): 12,654 ligands × 44 antitarget docking scores + mouse intravenous pLD50 (556,776 scores).

The dataset CSV is bundled in server/data/ (it is also auto-downloaded from TOX_DATASET_URL if absent), so the server runs offline and reproducibly. The expensive Vina-GPU docking was the one-time data generation step in the paper; its output is this dataset, so all analyses here are exact and fast — no GPU required.

Tools

Three tool names (dataset_overview, reproduce_all, reproduce_claims) collided with the heracleum-tox and cannabis-biopesticide servers, which are exposed to the agent at the same time. They are therefore registered under antitarget_-prefixed agent-visible names via @mcp.tool(name=...); the Python function names are unchanged, so server.tox_server.reproduce_all etc. still exist. The table below lists the wire names the agent must call.

Routing: two questions → two forward-only tool sequences

The intended usage is a sequence of natural scientific questions, not one "reproduce the paper" request. Each question starts at one canonical entry tool. The tools repeat the question's natural phrasing (EN + RU) in their docstrings for retrieval, then each nonterminal result returns only its immediate successor in metadata.next_tools. This makes the route deterministic and prevents reciprocal loops or skipped evidence.

The terminal protein_panel result has next_tools=[] and opens question 2 through metadata.next_question.entry_tool=cluster_correlation_heatmap. The terminal logp_confounder_analysis result has next_tools=[] and workflow_status=completed. See REPRODUCTION_QUESTIONS.md for the full scenario. Each recommended prompt explicitly asks the agent to return every generated figure artifact with its kind and SHA-256, rather than returning an orchestration log or a bare confirmation.

Reproducing the paper's assertions (not just numbers)

The tools return numbers; the paper's conclusions are an interpretation of them. The antitarget_reproduce_claims tool bridges this: for each of the paper's 11 assertions it returns the question that elicits it, the paper's claim, and a reproduced_statement (the claim restated with our numbers) plus the supporting evidence. In CoScientist the numbers flow ExperimentAgent (FEDOT.MAS runs the tool) → OrchestratorAgent (LLM writes the conclusion); the finding / reproduced_statement fields keep that synthesis faithful. See REPRODUCTION_QUESTIONS.md for the exact question list to ask CoScientist. The bulk "reproduce everything" tool is retained only as an audit fallback, not as the recommended user flow.

Each tool returns {"answer": ..., "metadata": ...}. Figures are saved as PNG to a local artifacts directory (TOX_ARTIFACTS_DIR) or, if S3 is configured, uploaded and returned as presigned URLs. metadata.figure includes artifact, kind, sha256, content_type and, for S3, bucket, key and expires_in, so callers can verify downloaded bytes. S3 is strict: partial credentials or an unavailable bucket fail startup, and upload errors propagate instead of silently returning a path inside the container. TOX_S3_ALLOW_LOCAL_FALLBACK=true is an explicit development-only opt-in to degraded local storage.

Related MCP server: skore-mcp

Reproduction fidelity

antitarget_reproduce_all and pytest tests/ assert these against the paper:

Documented, version-related deviations (faithful method; values differ slightly):

• NIH+Brenk: 5392 vs 5391 — a single molecule, from RDKit catalog version differences.

• Spearman median: ≈ −0.24 vs the figure's −0.14. The range matches exactly; the median is more negative because the published CSV is post-denoising (positive scores set to 0).

• Butina: the paper's 9665 clusters reproduce at Tanimoto distance ≈0.28 (similarity ≈0.72) with ECFP4/2048; the stated similarity threshold 0.65 yields ≈8260. Cluster counts are highly fingerprint/version-sensitive; the qualitative finding (high structural diversity,

  80% singletons, small largest cluster) is robust. The threshold is a tool parameter.

Run locally

git clone https://github.com/chemagents/tox-antitargets-mcp-server.git
cd tox-antitargets-mcp-server
cp .env.example .env
uv sync
uv run python -m server.tox_server     # serves http://0.0.0.0:7331/mcp

Run with Docker (standalone)

cp .env.example .env
docker compose up -d --build              # host port 7335 -> container 7331

This standalone mode needs no changes to CoScientist. To run it in the shared CoScientist/MinIO stack, follow COSCIENTIST_INTEGRATION.md; the separate Dockerfile.coscientist keeps the monorepo build context explicit.

Attach to CoScientist

CoScientist discovers MCP tools via RAG (Postgres + Qdrant). Register this server once:

# from the CoScientist repo root, with the RAG stack running and .env configured
python scripts/rag_tools/cli.py load mcp-servers/tox-antitargets-mcp-server/rag_registration.json
# or directly:
python scripts/rag_tools/cli.py add \
  --url http://localhost:7335/mcp \
  --name tox-antitargets \
  --description "Antitarget affinity vs rodent acute toxicity (LD50): is there a relationship between antitarget affinity and acute toxicity in mice; which molecular initiating events correlate with acute toxicity; does a strong affinity–LD50 correlation prove a mechanism or is it a logP confounder. Inverse docking, hERG/Bowes safety panel (Nikitin et al. 2025)"

After registration the ToolRetrieverAgent will surface these tools for toxicity / LD50 / mechanism-of-action queries, and ExperimentAgent (FEDOT.MAS) will call them by their URL. If CoScientist runs in the same Docker network, register the in-network URL instead: http://tox-antitargets-mcp-server:7331/mcp.

Tests

uv run pytest tests -v
uv run pytest tests -v -m "not slow"

License / data

MIT for code (see LICENSE). The dataset is released by the paper authors at chemagents/ld50-antitargets. Please cite Nikitin et al. (2025) when using these results.