Reproduce metabolite toxicology profiles with toxicity prediction, clustering, and cost estimation.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "heracleum-tox-mcp-server" yet — see the docs or source repo.
Using Heracleum sosnowskyi metabolite data, generate an in-silico toxicological profile including LD50 prediction, toxicity classification, and a summary of high-risk compounds.
Returns toxicity predictions, risk levels, and a list of high-risk metabolites.
Cluster Heracleum sosnowskyi metabolites in chemical space, identify structurally similar groups, and indicate which clusters may have higher toxicity.
Outputs clustering results, representative features for each cluster, and notes on potentially high-toxicity clusters.
Combine toxicity prediction with synthesis cost estimation to rank these metabolites and provide prioritized and deferred candidates for validation.
Provides a ranked list balancing risk and cost, with prioritized and secondary validation candidates.
Connect AI to life science databases and tools for bioinformatics workflows.
Search drug targets, compounds, papers, and trials for preclinical discovery research.
Search ChEMBL bioactivity data and compound properties from Claude or ChatGPT.
Access and combine biomedical RDF and API data through AI-friendly queries.
Search PubChem for compound data, properties, safety, and bioactivity links.
Expose chemical engineering calculations for AI-driven analysis and application integration.