Past the Wall

Orphan Finder: reliability-aware target prediction for natural products, drug repurposing, and antimicrobial resistance. Roughly half of all approved drugs come from natural products, yet for most natural products we still do not know which protein they act on. They are "orphan" molecules: demonstrably active, but with no known target, so they never advance. The standard way to predict a target is by chemical similarity, assuming a new molecule hits the same protein as a known drug it resembles. That approach fails silently for exactly the molecules that matter most, because orphan molecules, by definition, resemble nothing already characterized. We built Orphan Finder to measure and cross that boundary. Across 695,133 natural products, we quantified where similarity-based prediction collapses: below a Tanimoto similarity of 0.5, and 63% of natural-product space lies past that "analog wall." Orphan Finder always knows which side of the wall a prediction is on. Where a close analog exists it uses it; where none does, it stops guessing and switches to structure, docking the molecule into candidate protein pockets against real positive and negative controls. The entire pipeline is orchestrated with Claude Science, which assembles the compound libraries (COCONUT, ChEMBL, the Broad repurposing set), reads protein structures, dispatches GPU docking on Modal, and ranks the results with a calibrated confidence. Three findings show it works. First, an orphan natural product and a structurally unrelated repurposing-library compound (Tanimoto 0.28, no shared scaffold) both dock into the same pocket of M. tuberculosis InhA, the enzyme the frontline drug isoniazid targets, both engaging the catalytic Tyr158 and beating a matched decoy set, with an independent structure predictor confirming the poses. Second, because naming a target is not enough for an antibiotic, we added a mutation-aware layer that predicts whether binding survives resistance mutations; validated against known resistance, it then nominated seven orphan natural products predicted to hold their binding against DHFR precisely where the frontline drug fails. Third, the same engine generalizes to a second application: pointed at gut-microbe metabolites, it first re-discovered known microbe-to-host signaling (niacin to HCAR2, secondary bile acids to FXR and TGR5) as a positive control, then flagged a family of nine microbial carotenoid pigments all converging on the human xenobiotic receptor PXR, the master regulator of drug clearance, with every one docking within 1 kcal/mol of the canonical agonist rifampicin. Why it matters: antimicrobial resistance and undruggable natural products are two of the largest bottlenecks in finding new medicines. A target-prediction tool that reports its own reliability, and that asks whether a target will still be druggable after resistance emerges, turns a flood of orphan molecules into a ranked, structure-backed, resistance-aware set of testable hypotheses. Every result is a computational nomination for wet-lab follow-up, and everything is fully open and reproducible from the repository.