Journal
CHEMICAL SCIENCE
Volume 11, Issue 7, Pages 1775-1797Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/c9sc04336e
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Funding
- 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
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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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