Transitive prediction of small-molecule function through alignment of high-content screening resources.

ABSTRACT: High-content image-based phenotypic screens (HCSs) provide a scalable approach to characterize biological functions of compounds. The widespread adoption of HCS has led to a growing body of available profile datasets. However, study-specific experimental and computational choices lead to profile datasets that cannot be directly combined. A critical, long-standing challenge is how to integrate these rich but currently isolated HCS dataset resources. Here we introduce a contrastive, deep-learning framework that leverages sparse sets of overlapping profiles as fiducials to align heterogeneous HCS profile datasets in a shared latent space. We demonstrate that this alignment facilitates accurate 'transitive' predictions, whereby the function of an uncharacterized compound screened in one datase

SUBMITTER: Bao F 

PROVIDER: S-EPMC12846546 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature