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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

Title
Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data
Authors
Lee, HanbiKim, Wankyu
Ewha Authors
김완규
SCOPUS Author ID
김완규scopus
Issue Date
2019
Journal Title
PHARMACEUTICS
ISSN
1999-4923JCR Link
Citation
PHARMACEUTICS vol. 11, no. 8
Keywords
drug target interactiondeep neural networkdrug-induced transcriptome datadrug repositioning
Publisher
MDPI
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
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 naive Bayes, random forest, or logistic regression.
DOI
10.3390/pharmaceutics11080377
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자연과학대학 > 생명과학전공 > Journal papers
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