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    Filter Compounds For Drug-Likeness

    Filter Compounds For Drug-Likeness: a medchem recipe chaining admet_ai.

    recipesmedchem

    Generated recipe

    Seeded from a curated quick-action prompt. The prompt below runs the real routing — copy it, change the target, and run.

    Recommended pairing

    These tools are commonly run together — they cover complementary ground on the same input.

    Best fit: filter compounds for drug-likeness — a medchem goal.

    The prompt

    Search ChEMBL for JAK2 inhibitors with IC50 < 100nM, run medchem filters (Lipinski, PAINS, NIBR, Lilly demerits) on the top 30, run ADMET-AI on the survivors, then dock the top 5 filter-passing compounds against JAK2 (PDB: 4GI3) with DiffDock and rescore with MM-GBSA for consensus - load all data into the sandbox and use RDKit to compute Murcko scaffolds, generate a trellis plot with matplotlib showing each scaffold's filter pass rate × ADMET score × consensus docking confidence, and recommend the top 3 compounds for a JAK2-selective lead series?

    What each tool contributes

    • medchem filter — Drug-likeness, PAINS, structural alerts, Lilly demerits
    • admet_ai — 50+ ADMET endpoints on filtered compounds

    Why these tools pair

    MedChem filters eliminate structural liabilities first, then ADMET predicts pharmacokinetic properties on the survivors — efficient funnel

    Data flow

    Every tool operates on the same input: smiles_array.

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