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    Target scout: ChEMBL compounds, drugs and PubMed papers

    1

    From a target name or UniProt ID: the ChEMBL target, most potent compounds (CSV), approved and clinical drugs, and recent PubMed papers.

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    Target scout: ChEMBL compounds, drugs and PubMed papers

    Target scout: ChEMBL compounds, drugs and PubMed papers

    Example session with this skill installed

    What's known about EGFR? Pull the potent compounds and the approved drugs.

    • Read your context and instructions
    • Compiled the target scout
    • Generated the data export

    Summary

    Target: CHEMBL203 - Epidermal growth factor receptor (Homo sapiens, SINGLE PROTEIN). UniProt P00533, gene EGFR.
    Found: 70 drugs at phase 1 or above (15 approved), the most potent compounds as a CSV, and 4 recent papers.

    Approved drugs (15 of 70)

    ChEMBL IDnamestatusaction
    CHEMBL1173655AFATINIBapprovedINHIBITOR
    CHEMBL4297774AMIVANTAMABapprovedINHIBITOR
    CHEMBL3545311BRIGATINIBapprovedINHIBITOR
    CHEMBL1201577CETUXIMABapprovedINHIBITOR
    CHEMBL553ERLOTINIBapprovedINHIBITOR
    CHEMBL939GEFITINIBapprovedINHIBITOR
    CHEMBL554LAPATINIBapprovedINHIBITOR
    CHEMBL3353410OSIMERTINIBapprovedINHIBITOR

    (7 more approved, plus 55 at phase 1-3, in the full report)

    Potent compounds

    Written to litscout_EGFR_compounds.csv with columns id, smiles, pchembl, standard_type, standard_value_nM, assay, year, document. Because it has a smiles and an id column, it feeds straight into a triage or ADMET run without editing.

    Recent literature

    The newest PubMed hits for "EGFR inhibitor", each with title, journal, year, PMID and link.

    Next steps

    1. Triage the compound CSV to drop anything with PAINS or Brenk alerts before you model it.
    2. Note the approved list is split between small molecules (erlotinib, osimertinib) and antibodies (cetuximab, amivantamab); filter by molecule type if you only want chemical matter.
    3. Potency values come from different assays, so treat the ranking as a shortlist, not a leaderboard.

    Files: litscout_EGFR.md, litscout_EGFR_compounds.csv

    Real output from a live ChEMBL and PubMed run.

    litscout_EGFR_compounds.csv

    CSV · data export

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Ask your agent what is known about a target and get a sourced briefing back in under a minute.

    What it does

    • Resolve: maps a target name, gene symbol or UniProt accession to a ChEMBL entry, preferring the human SINGLE PROTEIN. This matters: a plain search for "EGFR" returns PPI complexes and the mouse protein above the human one.
    • Potent compounds: pulls IC50/Ki activities in nM, keeps the best pChEMBL per compound, and writes a CSV with id, SMILES, pChEMBL, assay and year.
    • Drug landscape: lists drugs at phase 1 and above with their mechanism and action, approved ones first.
    • Literature: the newest PubMed papers for the target, with title, journal, year, PMID and link.
    • Output: a Markdown report plus a compound CSV that feeds straight into triage or ADMET (the triage script is bundled).

    Why it's trustworthy

    Everything comes from the public ChEMBL REST API and NCBI E-utilities, fetched by a bundled, self-tested script (scripts/litscout.py --selftest); the model never recalls a compound or a paper from memory. If one source fails, the report says so rather than quietly dropping it.

    Requirements

    uv and internet access. Standard library only, no API keys, no paid data sources. Requests are rate-limited and identify themselves.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Trust & safety

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    Verified clean 9 days ago

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    Python

    Listed9 days ago

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