Pluto Bioinformatics

GSE129143: A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules (RNA-Seq)

Bulk RNA sequencing

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 a multi-omics approach for uncovering MoAs through an interpretable machine learning model of the effects of compounds on transcriptomic, epigenomic, metabolomic, and proteomic data. We applied this approach to examine compounds with beneficial effects in models of Huntingtons disease, finding common MoAs for previously unrelated compounds that were not predicted based on similarities in the compounds structures, connectivity scores, or binding targets. We experimentally validated two such disease-relevant MoAs, autophagy activation and bioenergetics manipulation. This interpretable machine learning approach can be used to find and evaluate MoAs in future drug development efforts. SOURCE: Natasha Patel-Murray Massachusetts Institute of Technology