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 dataset can be predicted through comparison with characterized compounds already profiled in other datasets. In silico alignment of HCS resources provides a path to unify fast-growing HCS resources and accelerate early drug discovery efforts.