DrugReflector
DeMeo B, Nesbitt C, Miller SA, Burkhardt DB, Lipchina I, Fu D, Holderrieth P, Kim D, Kolchenko S, Szalata A, Gupta I, Kerr C, Pfefer T, Rojas-Rodriguez R, Kuppassani S, Kruidenier L, Doshi PB, Zamanighomi M, Collins JJ, Shalek AK, Theis FJ, Cortes M
2025 · Science (New York, N.Y.)
Phenotypic drug screening remains constrained by the vastness of chemical space and the technical challenges of scaling experimental workflows.
Abstract
From the original paper, Science (New York, N.Y.) · PubMed
Phenotypic drug screening remains constrained by the vastness of chemical space and the technical challenges of scaling experimental workflows. To overcome these barriers, computational methods have been developed to prioritize compounds, but they rely on either single-task models lacking generalizability or heuristic-based genomic proxies that resist optimization. We designed an active deep learning framework that leverages omics to enable scalable, optimizable identification of compounds that induce complex phenotypes. Our generalizable algorithm outperformed state-of-the-art models on classical recall, translating to a 13- to 17-fold increase in phenotypic hit rate across two hematological discovery campaigns. Combining this algorithm with a lab-in-the-loop signature refinement step, we achieved an additional twofold increase in hit rate along with molecular insights. In sum, our framework enables efficient phenotypic hit identification campaigns, with broad potential to accelerate drug discovery.
Summary
Editorial summary pending review by the maintainer. The paper's own abstract appears above; the Atlas summary in the maintainer's voice will explain how DrugReflector relates to the cross-modality inverse-design framework of the review.
Why this level
Level 2 because the method instantiates the inverse-design objective through an empirical or learned proxy response map rather than a mechanistic intervention-dependent model. Representation family is signature / state-matching. Cited in §3.3 of the review. Editorial rationale pending review by the maintainer.