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    SAR report: scaffolds, matched pairs and activity cliffs

    1

    Turn a compound + activity CSV into scaffold groups, matched-pair transformations, activity cliffs and next compounds to make.

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    SAR report: scaffolds, matched pairs and activity cliffs

    SAR report: scaffolds, matched pairs and activity cliffs

    Example session with this skill installed

    Run a SAR analysis on this EGFR series (IC50 in nM) and tell me what drives potency.

    • Read your context and instructions
    • Compiled the sar report
    • Generated the data export

    Summary

    37 compounds analysed (0 skipped). pActivity from IC50_nM: min 4.00, median 6.45, mean 6.76, max 10.54, SD 1.53.
    5 Murcko scaffolds, 126 matched pairs (82 distinct transformations), 14 activity cliffs (Tanimoto >= 0.7, |delta p| >= 1.0).

    Most potent: CHEMBL29197 (10.54), CHEMBL420624 (10.00), CHEMBL328216 (9.92), CHEMBL93032 (9.77), CHEMBL52765 (9.10)

    Scaffolds

    scaffoldcountmean_pmax_pbest_id
    c1ccc(Nc2ncnc3ccccc23)cc1227.2610.54CHEMBL29197
    c1ccc(CNc2ncnc3ccccc23)cc1126.138.00CHEMBL325245
    c1ccc(Oc2ncnc3ccccc23)cc116.126.12CHEMBL117710
    c1ccc(CCNc2ncnc3ccccc23)cc115.395.39CHEMBL117804
    c1ccc(Nc2ccnc3ccccc23)cc115.265.26CHEMBL37346

    Read: the direct 4-anilinoquinazoline link is worth about 1.1 log over the benzylamine (CH2) version and 1.9 over the homologated CCN. Swapping the quinazoline N3 for CH (last row) costs ~2 log.

    Top MMP transformations

    transformcountmean delta_pminmax
    O=N+* >> N*3+2.32-0.09+4.00
    c1ccc(C*)cc1 >> Brc1cccc(*)c18+1.38-0.24+3.25
    O=N+* >> H5+1.38+1.12+1.57
    O* >> CO*2+1.26+0.75+1.77
    CO* >> N*5+0.85-0.85+2.64

    Read: reducing the nitro to an amine is the single biggest consistent gain. The meta-halogen aniline (Br) beats the benzyl across 8 pairs, which is the classic EGFR meta-halide preference.

    Activity cliffs (top by SALI)

    id_lowid_hightanimotop_lowp_highdelta_psali
    CHEMBL289787CHEMBL291970.7116.0110.544.5315.7
    CHEMBL52197CHEMBL291970.7176.4610.544.0814.4
    CHEMBL333851CHEMBL1157450.9076.237.251.0211.0

    Next compounds to make

    1. Apply the nitro -> amine reduction to the best unreduced quinazoline in the series: it is the only transformation with a mean above +2 log.
    2. Put the meta-Br aniline on the direct N-linked scaffold, combining the two independent gains.
    3. Resynthesise and retest CHEMBL289787 before trusting the 4.5-log cliff: a jump that large on 0.71 similarity is as likely to be an assay artefact as real SAR.

    Files: sar_report.md, scaffolds.csv, mmp_transforms.csv, cliffs.csv (+ 2 more).

    Real output: 37 EGFR quinazolines from ChEMBL document CHEMBL1128536.

    scaffolds.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

    Give your agent a CSV of compounds with activities and get a medicinal-chemistry SAR report back.

    What it does

    • Normalise: converts IC50/Ki in nM to pActivity (9 - log10 nM), standardizes structures and averages replicate measurements of the same compound.
    • Scaffolds: groups by Murcko scaffold with count, mean and max pActivity, and the best compound in each group.
    • Matched pairs: finds matched molecular pairs with RDKit rdMMPA single cuts plus H->X sites, keeps small changes only, and ranks transformations by mean delta pActivity with counts and spread.
    • Activity cliffs: pairs with Morgan2 Tanimoto >= 0.7 and |delta p| >= 1.0, ranked by SALI.
    • Output: a Markdown report, six CSVs and an optional cliff plot.

    Why it's trustworthy

    All the chemistry and statistics run in a bundled, self-tested script (scripts/sar.py --selftest); the model never estimates a scaffold or a delta. Transformations seen only once are labelled as such, so single observations are not presented as trends.

    The agent closes every report with 2-3 concrete compounds to make next, each tied to a transformation in your own data.

    Requirements

    uv. RDKit and matplotlib install automatically on first run. Input is any CSV with a SMILES column, an id column and an activity column.

    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

    Security scanned

    Verified clean 9 days ago

    • Passed all security checks, Safe to install

    Requires

    Python

    Listed9 days ago

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