drug-discovery

Drug discovery data retrieval is automated by querying free public APIs like ChEMBL, PubChem, and OpenFDA to fetch target bioactivity, molecular properties, and safety signals without authentication. Results are summarized to support medicinal chemistry decisions.

To check Lipinski and Veber rules, the Skill retrieves molecular properties from PubChem and evaluates them against Rule of Five and Veber parameters. This assesses drug-likeness and oral bioavailability to help shortlist candidate molecules for synthesis.

Yes, you can screen drug interactions and safety signals without API keys. The Skill queries OpenFDA for drug interactions and OpenTargets for target-disease associations using free public APIs that require no authentication for access.

Finding bioactive compounds in ChEMBL involves querying a specified drug target to fetch top bioactive molecules. The Skill automates this target search and bioactivity exploration to accelerate pharmaceutical research workflows.

Integrating bioactivity and property data for candidate selection is done by combining ChEMBL bioactivity results, PubChem molecular properties, and OpenFDA safety signals in a single workflow. This consolidated approach supports rapid shortlisting of candidate molecules for medicinal chemistry decisions.

Yes, the Skill supports end-to-end medicinal chemistry workflows by covering target search, molecule property evaluation, and safety screening. It retrieves data, performs basic analytics, and provides decision support across the entire drug discovery process.