Figure 1. AiChemy is a multi-agent supervisor comprising external MCP servers PubChem, PubMed, and OpenTargets, and Databricks-managed MCP servers of Genie Space (text-to-SQL for DrugBank structured data) and of Vector Search (for unstructured data like ZINC molecular embeddings). Skills can also be loaded to specify task sequence and report formatting and style to ensure consistent output.

Its key capabilities include identifying disease targets and drug candidates, retrieving their detailed chemical, pharmacokinetics properties, and providing safety and toxicity assessments. Crucially, AiChemy backs its findings with supporting evidence traceable to verifiable data sources, making it ideal for research.

The Guided Tasks panel provides necessary prompts and agent Skills to perform the key steps in a drug discovery workflow of disease -> target -> drug -> literature validation.

To identify a follow-up to the oral Selective Estrogen Receptor Degrader (SERD) approved in 2023, Elacestrant, we can leverage chemical similarity. We search the large ZINC15 chemical library for drug-like molecules structurally similar to Elacestrant, as Quantitative Structure–Activity Relationship (QSAR) principles suggest they will share similar properties. This is achieved by querying Databricks Vector Search, which uses the 1024-bit Extended-Connectivity Fingerprint (ECFP) molecular embedding of Elacestrant (as query vector) to find the most similar embeddings within ZINC's 250,000-molecule index.