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New D2ML models enhance direct-to-biology small molecule screening

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new machine learning approach called D2ML for direct-to-biology small molecule screening. This method is designed to handle the unique challenges of D2B data, such as uncertain concentrations, to improve predictions and guide the discovery of more effective compounds. D2ML models can predict activity from single-point screens and relative potency across targets, even with crude dose-response assays. In experiments, D2ML guided the selection of new compounds that demonstrated improved potency and selectivity compared to existing molecules. AI

IMPACT Enhances AI's role in drug discovery by improving the efficiency and effectiveness of small molecule screening.

RANK_REASON The cluster contains a research paper detailing a new machine learning model and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New D2ML models enhance direct-to-biology small molecule screening

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The cluster contains a research paper detailing a new machine learning model and methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · 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

    arXiv:2610.41070v1 Announce Type: cross Abstract: Direct-to-biology (D2B) screening enables rapid testing of newly synthesized molecules without purification. However, uncertain concentrations in crude mixtures and limited purified-compound measurements complicate the use of thes…

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