LAPSE:2023.35366
Published Article
LAPSE:2023.35366
Computational Models That Use a Quantitative Structure−Activity Relationship Approach Based on Deep Learning
April 28, 2023
In the toxicological testing of new small-molecule compounds, it is desirable to establish in silico test methods to predict toxicity instead of relying on animal testing. Since quantitative structure−activity relationships (QSARs) can predict the biological activity from structural information for small-molecule compounds, QSAR applications for in silico toxicity prediction have been studied for a long time. However, in recent years, the remarkable predictive performance of deep learning has attracted attention for practical applications. In this review, we summarize the application of deep learning to QSAR for constructing prediction models, including a discussion of parameter optimization for deep learning.
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Keywords
bioinformatics, computational models, convolution neural network, deep learning, graph convolutional networks, parameter optimization, quantitative structure–activity relationship
Suggested Citation
Matsuzaka Y, Uesawa Y. Computational Models That Use a Quantitative Structure−Activity Relationship Approach Based on Deep Learning. (2023). LAPSE:2023.35366
Author Affiliations
Matsuzaka Y: Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose 204-8588, Japan; Division of Molecular and Medical Genetics, Center for Gene and Cell Therapy, The Institute of Medical Science, The University of Tokyo, Minato-ku 108-86
Uesawa Y: Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose 204-8588, Japan [ORCID]
Uesawa Y: Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose 204-8588, Japan [ORCID]
Journal Name
Processes
Volume
11
Issue
4
First Page
1296
Year
2023
Publication Date
2023-04-21
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr11041296, Publication Type: Review
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LAPSE:2023.35366
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doi:10.3390/pr11041296
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[v1] (Original Submission)
Apr 28, 2023
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Apr 28, 2023
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