drug-discovery

You can search ChEMBL for targets and retrieve bioactivity data to identify lead compounds with favorable pChEMBL values. Running a query like 'fetch top bioactive compounds with pChEMBL ≥ 6' compiles a prioritized short list of candidate molecules.

Yes, you can compute Lipinski Ro5, Veber rules, TPSA, and QED-like metrics for compounds retrieved from ChEMBL. These drug-likeness and ADMET assessments help prioritize candidates by evaluating their molecular properties and predicted suitability.

Yes, the workflow leverages OpenFDA and OpenTargets via Python scripts and curl calls. It looks up drug interactions via OpenFDA and gathers disease associations via OpenTargets to inform risk and benefit analyses during target evaluation.

The best way is to query public chemistry databases like ChEMBL and PubChem to assemble a short list of candidate molecules. You then gather basic properties, compute drug-likeness metrics, and check safety data to inform your medicinal chemistry decisions.

You can gather basic properties by fetching data from PubChem and retrieving bioactivity data from ChEMBL. Safety and target-context interpretation is achieved by looking up drug interactions via OpenFDA and disease associations via OpenTargets to inform prioritization.

Limitations include dependency on the availability and update frequency of open-data sources like ChEMBL, PubChem, OpenFDA, and OpenTargets. Results are constrained by the accuracy of public records and the script's ability to parse external API responses correctly.