An MCP server that reproduces the results of Nikitin et al., 'Towards Explainable Computational Toxicology: Linking Antitargets to Rodent Acute Toxicity' as callable tools, enabling users to compute toxicity predictions and mechanistic analyses deterministically from a bundled dataset.
MCP-native scientific skills for reproducible computational biology and AI-driven drug-discovery workflows. It combines deterministic scientific tools with an MCP server to give AI agents real computational capabilities.
A self-driving cheminformatics MCP server that dynamically exposes a growing library of RDKit-based molecular analysis tools (fingerprints, descriptors, substructure matching, drug-likeness filters, and more) as MCP tools, with each skill autonomously implemented and tested by an agent loop without human intervention.
ChemMCP is an easy-to-use and extensible chemistry toolkit for LLMs and AI assistants, enabling molecular analysis, property prediction, and reaction synthesis tasks without domain-specific training.
Exposes EPA Computational Toxicology (CompTox) evidence federation through MCP, enabling chemical identity, hazard, exposure, and bioactivity data retrieval for AI agents.
An MCP server that gives LLMs native access to cheminformatics and molecular ML tools, enabling molecular structure manipulation, descriptor calculation, ML model training, and analysis report generation through natural conversation.
