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Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data

Department of Life Sciences, College of Natural Science, Ewha Womans University, Seoul 03760, Korea
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Author to whom correspondence should be addressed.
Pharmaceutics 2019, 11(8), 377; https://doi.org/10.3390/pharmaceutics11080377
Received: 12 June 2019 / Revised: 16 July 2019 / Accepted: 24 July 2019 / Published: 2 August 2019
(This article belongs to the Special Issue Computational Drug Repurposing)
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Abstract

Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein–protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction under the same evaluation scheme. Among the three target features, the PM and the PPI network show similar performances superior to GEPs. DNN models based on both features consistently outperformed other machine learning methods such as naïve Bayes, random forest, or logistic regression. View Full-Text
Keywords: drug target interaction; deep neural network; drug-induced transcriptome data; drug repositioning drug target interaction; deep neural network; drug-induced transcriptome data; drug repositioning
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Lee, H.; Kim, W. Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data. Pharmaceutics 2019, 11, 377.

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