期刊
CHEMICAL SCIENCE
卷 11, 期 7, 页码 1775-1797出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/c9sc04336e
关键词
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资金
- National Heart, Lung, and Blood Institute of the National Institutes of Health [K99HL138272, R00HL138272]
- National Institutes of Neurological Diseases of the National Institutes of Health [R3509730]
- National Institutes of Health [HL61795, HG007690, HL119145]
- AHA [2017D007382]
- Frederick National Laboratory for Cancer Research, National Institutes of Health [HHSN261200800001E]
- Intramural Research Program of NIH, Frederick National Lab, Center for Cancer Research
Without foreknowledge of the complete drug target information, development of promising and affordable approaches for effective treatment of human diseases is challenging. Here, we develop deepDTnet, a deep learning methodology for new target identification and drug repurposing in a heterogeneous drug-gene-disease network embedding 15 types of chemical, genomic, phenotypic, and cellular network profiles. Trained on 732 U.S. Food and Drug Administration-approved small molecule drugs, deepDTnet shows high accuracy (the area under the receiver operating characteristic curve = 0.963) in identifying novel molecular targets for known drugs, outperforming previously published state-of-the-art methodologies. We then experimentally validate that deepDTnet-predicted topotecan (an approved topoisomerase inhibitor) is a new, direct inhibitor (IC50 = 0.43 mu M) of human retinoic-acid-receptor-related orphan receptor-gamma t (ROR-gamma t). Furthermore, by specifically targeting ROR-gamma t, topotecan reveals a potential therapeutic effect in a mouse model of multiple sclerosis. In summary, deepDTnet offers a powerful network-based deep learning methodology for target identification to accelerate drug repurposing and minimize the translational gap in drug development.
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