A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules
Name
s41598-020-57691-7.pdf
Description
Published version
Size
1.91 MB
Format
Adobe PDF
Checksum (MD5)
06452af53c66097da0af2f8d8b5a520b
Author(s)
Patel-Murray, Natasha Leanna
Adam, Miriam
Huynh, Nhan C
Wassie, Brook T.
Milani, Pamela
Fraenkel, Ernest
Date Issued
January 2020
Journal
Scientific Reports
Publisher
Springer Science and Business Media LLC
Citation
Patel-Murray, Natasha L. et al. “A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules.” Scientific Reports, vol. 10, 2020, article 954 © 2020 The Author(s)
Version
Final published version
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 omics was essential, as some MoAs were virtually undetectable in specific assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41598-020-57691-7
