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Open AccessArticle

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap–Deep Learning

1
Department of Medical Molecular Informatics, Meiji Pharmaceutical University, 204-8588 Tokyo, Japan
2
Laboratory of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka 422-8529, Japan
*
Author to whom correspondence should be addressed.
Molecules 2020, 25(6), 1317; https://doi.org/10.3390/molecules25061317
Received: 17 February 2020 / Revised: 5 March 2020 / Accepted: 9 March 2020 / Published: 13 March 2020
(This article belongs to the Special Issue Integrated QSAR)
The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals contributes to the development and propagation of steatosis and hepatotoxicity. However, the mechanisms for AhR-induced hepatotoxicity and tumor propagation in the liver remain to be revealed, due to the wide variety of AhR ligands. Recently, quantitative structure–activity relationship (QSAR) analysis using deep neural network (DNN) has shown superior performance for the prediction of chemical compounds. Therefore, this study proposes a novel QSAR analysis using deep learning (DL), called the DeepSnap–DL method, to construct prediction models of chemical activation of AhR. Compared with conventional machine learning (ML) techniques, such as the random forest, XGBoost, LightGBM, and CatBoost, the proposed method achieves high-performance prediction of AhR activation. Thus, the DeepSnap–DL method may be considered a useful tool for achieving high-throughput in silico evaluation of AhR-induced hepatotoxicity. View Full-Text
Keywords: chemical structure; aryl hydrocarbon receptor; DeepSnap; deep learning; QSAR; machine learning chemical structure; aryl hydrocarbon receptor; DeepSnap; deep learning; QSAR; machine learning
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Matsuzaka, Y.; Hosaka, T.; Ogaito, A.; Yoshinari, K.; Uesawa, Y. Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap–Deep Learning. Molecules 2020, 25, 1317.

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