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arXiv Machine Learning By AkshatKumar Nigam, Neri Amara, Hillary Dequina, Dyana Kenanova, Seth Malmersjo, Fiorella Ruggiu, Raymond V. Fucini, Jack Sadowsky, Stig K. Hansen, Imran S. Haque

D2ML: Models for Direct-to-Biology Small Molecule Screening

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The paper introduces D2ML, a set of machine‑learning models designed for Direct‑to‑Biology (D2B) small‑molecule screening. These assay‑aware models predict activity from single‑point screens, relative potency across targets from crude dose‑response assays, and absolute potency from purified‑compound measurements, and a joint model that shares information across stages improves predictions, especially for crude dose‑response selectivity. Using these models, the authors guided the synthesis of 84 new compounds that extended the Pareto front of potency and selectivity, achieving a ROS1 IC₅₀ of 14.7 nM with 233‑fold selectivity over TRKB, surpassing the best training compound.

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