I'll start by discovering the relevant skills and database access for ChEMBL and medicinal-chemistry filtering.
Discovering how to pull measured EGFR inhibitor activity from ChEMBL Discovering the clustering and diversity workflow Discovering structural-alert and drug-likeness filtering guidance
Let me load the key skills: ChEMBL access, medchem triage, chemical-space analysis, and RDKit.
Understanding ChEMBL query syntax Understanding how to normalize bioactivity data Understanding drug-likeness filtering Understanding embedding and clustering workflow Understanding RDKit fingerprint and scaffold APIs
Skills loaded. Now let me check network reach to ChEMBL and identify the EGFR target.
ChEMBL is reachable. Now let me pull the measured activity set for human EGFR (CHEMBL203).
Sizing the EGFR measured-activity set available from ChEMBL
20,949 measured records. Let me page the full set and save it locally.
Pulling the full measured EGFR activity set to disk for curation
All 20,949 pulled. Now let me curate the measurements and aggregate to one potency per compound.
Reducing measurements to one curated potency per EGFR inhibitor
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