A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules.

ABSTRACT: High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. Examining compounds with beneficial effects in models of Huntington's Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. The approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these effects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. The use of multiple

SUBMITTER: Patel-Murray NL 

PROVIDER: S-EPMC6976599 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature