A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules
作者: Natasha L. Patel-MurrayMiriam AdamNhan HuynhBrook T. WassiePamela MilaniErnest Fraenkel
作者单位: 1Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 02139, Cambridge, MA, USA
2Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 02139, Cambridge, MA, USA
3Broad Institute, 02139, Cambridge, MA, USA
刊名: Scientific Reports, 2020, Vol.10 (1), pp.424-43.e7
来源数据库: Springer Nature Journal
DOI: 10.1038/s41598-020-57691-7
英文摘要: Abstract(#br)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...
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影响因子:2.927 (2012)

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