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Issue 7, 2020
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Target identification among known drugs by deep learning from heterogeneous networks

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Abstract

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 μM) of human retinoic-acid-receptor-related orphan receptor-gamma t (ROR-γt). Furthermore, by specifically targeting ROR-γt, 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.

Graphical abstract: Target identification among known drugs by deep learning from heterogeneous networks

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Supplementary files

Article information


Submitted
28 Aug 2019
Accepted
09 Jan 2020
First published
13 Jan 2020

This article is Open Access
All publication charges for this article have been paid for by the Royal Society of Chemistry

Chem. Sci., 2020,11, 1775-1797
Article type
Edge Article

Target identification among known drugs by deep learning from heterogeneous networks

X. Zeng, S. Zhu, W. Lu, Z. Liu, J. Huang, Y. Zhou, J. Fang, Y. Huang, H. Guo, L. Li, B. D. Trapp, R. Nussinov, C. Eng, J. Loscalzo and F. Cheng, Chem. Sci., 2020, 11, 1775
DOI: 10.1039/C9SC04336E

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