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<p>The discovery of bioactive small molecules is dominated by iterative design-make-purify-test cycles focused on specific protein targets. In contrast, phenotype-driven discovery can yield bioactive molecules with unexpected mechanisms of action, and open paths to first-in-class drugs. Here, we present a fully closed-loop, algorithm-driven workflow for phenotypic-driven molecular discovery. Initially, a large virtual reaction space is constructed from pairs of potential substrates and co-substrates. Batches of reactions are then algorithmically-designed and automatically executed, and the products screened in a phenotypic assay; based on observed hits, the algorithm then directs subsequent round(s) of discovery and optimisation until a user-defined end-point is reached. The approach was exemplified using Rh-catalysed annulations of hydoxamate esters with alkene/alkyne co-substrates, coupled with the cell painting assay, and enabled the discovery and structural evolution of a series of tubulin modulators. Because the workflow is agnostic to both the chemistry and the assay modality, the approach may be generalisable for the automated function-directed exploration of synthetically-accessible chemical space. The approach has the potential to accelerate the discovery of chemical probes and to unlock opportunities for drug discovery.</p><p></p>

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Algorithm-driven, phenotype-directed bioactive molecular discovery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月16日

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  • Amalia-Sofia Piticari,
  • Samuel D. Griggs,
  • Laura Crawford,
  • Joss Whittle,
  • Brian S Mantilla,
  • Sonja Sievers,
  • Megan H. Wright,
  • Stephen P. Marsden,
  • Mark Basham,
  • Adam Nelson

摘要

The discovery of bioactive small molecules is dominated by iterative design-make-purify-test cycles focused on specific protein targets. In contrast, phenotype-driven discovery can yield bioactive molecules with unexpected mechanisms of action, and open paths to first-in-class drugs. Here, we present a fully closed-loop, algorithm-driven workflow for phenotypic-driven molecular discovery. Initially, a large virtual reaction space is constructed from pairs of potential substrates and co-substrates. Batches of reactions are then algorithmically-designed and automatically executed, and the products screened in a phenotypic assay; based on observed hits, the algorithm then directs subsequent round(s) of discovery and optimisation until a user-defined end-point is reached. The approach was exemplified using Rh-catalysed annulations of hydoxamate esters with alkene/alkyne co-substrates, coupled with the cell painting assay, and enabled the discovery and structural evolution of a series of tubulin modulators. Because the workflow is agnostic to both the chemistry and the assay modality, the approach may be generalisable for the automated function-directed exploration of synthetically-accessible chemical space. The approach has the potential to accelerate the discovery of chemical probes and to unlock opportunities for drug discovery.