Target identification among known drugs by deep learning from heterogeneous networks.

Chemical Science
Xiangxiang ZengFeixiong Cheng

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 dru...Continue Reading

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Citations

Nov 7, 2020·Trends in Pharmacological Sciences·Zhenyu WuQin Ma
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Jul 9, 2021·Frontiers in Genetics·Lei XuRong Song
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Aug 4, 2021·Briefings in Functional Genomics·Linhui YuXiangxiang Zeng
Nov 27, 2021·Interdisciplinary Sciences, Computational Life Sciences·Xuting ZhangZunnan Huang

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Methods Mentioned

BETA
circular dichroism
ELISA
feature extraction
affinity purification
RNA-seq
interaction prediction
light scattering

Software Mentioned

NeoDTI
TreeBagger
LIBSVM
Phoenix WinNonlin
deepDTnet
ChEMBL
AutoDock Vina
Open Babel
MATLAB
GOSemSim

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