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Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning

[Image: see text] The toxicity, absorption, distribution, metabolism, and excretion properties of some targets are difficult to predict by quantitative structure–activity relationship analysis. Therefore, there is a need for a new prediction method that performs well for these targets. The aim of th...

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Autores principales: Mamada, Hideaki, Nomura, Yukihiro, Uesawa, Yoshihiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9134387/
https://www.ncbi.nlm.nih.gov/pubmed/35647436
http://dx.doi.org/10.1021/acsomega.2c00261
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author Mamada, Hideaki
Nomura, Yukihiro
Uesawa, Yoshihiro
author_facet Mamada, Hideaki
Nomura, Yukihiro
Uesawa, Yoshihiro
author_sort Mamada, Hideaki
collection PubMed
description [Image: see text] The toxicity, absorption, distribution, metabolism, and excretion properties of some targets are difficult to predict by quantitative structure–activity relationship analysis. Therefore, there is a need for a new prediction method that performs well for these targets. The aim of this study was to develop a new regression model of rat clearance (CL). We constructed a regression model using 1545 in-house compounds for which we had rat CL data. Molecular descriptors were calculated using molecular operating environment, alvaDesc, and ADMET Predictor software. The classification model of DeepSnap and Deep Learning (DeepSnap-DL) with images of the three-dimensional chemical structures of compounds as features was constructed, and the prediction probabilities for each compound were calculated. For molecular descriptor-based methods that use molecular descriptors and conventional machine learning algorithms selected by DataRobot, the correlation coefficient (R(2)) and root mean square error (RMSE) were 0.625–0.669 and 0.295–0.318, respectively. We combined molecular descriptors and prediction probability of DeepSnap-DL as features and developed a novel regression method we called the combination model. In the combination model with these two types of features and conventional algorithms selected by DataRobot, R(2) and RMSE were 0.710–0.769 and 0.247–0.278, respectively. This finding shows that the combination model performed better than molecular descriptor-based methods. Our combination model will contribute to the design of more rational compounds for drug discovery. This method may be applicable not only to rat CL but also to other pharmacokinetic and pharmacological activity and toxicity parameters; therefore, applying it to other parameters may help to accelerate drug discovery.
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spelling pubmed-91343872022-05-27 Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning Mamada, Hideaki Nomura, Yukihiro Uesawa, Yoshihiro ACS Omega [Image: see text] The toxicity, absorption, distribution, metabolism, and excretion properties of some targets are difficult to predict by quantitative structure–activity relationship analysis. Therefore, there is a need for a new prediction method that performs well for these targets. The aim of this study was to develop a new regression model of rat clearance (CL). We constructed a regression model using 1545 in-house compounds for which we had rat CL data. Molecular descriptors were calculated using molecular operating environment, alvaDesc, and ADMET Predictor software. The classification model of DeepSnap and Deep Learning (DeepSnap-DL) with images of the three-dimensional chemical structures of compounds as features was constructed, and the prediction probabilities for each compound were calculated. For molecular descriptor-based methods that use molecular descriptors and conventional machine learning algorithms selected by DataRobot, the correlation coefficient (R(2)) and root mean square error (RMSE) were 0.625–0.669 and 0.295–0.318, respectively. We combined molecular descriptors and prediction probability of DeepSnap-DL as features and developed a novel regression method we called the combination model. In the combination model with these two types of features and conventional algorithms selected by DataRobot, R(2) and RMSE were 0.710–0.769 and 0.247–0.278, respectively. This finding shows that the combination model performed better than molecular descriptor-based methods. Our combination model will contribute to the design of more rational compounds for drug discovery. This method may be applicable not only to rat CL but also to other pharmacokinetic and pharmacological activity and toxicity parameters; therefore, applying it to other parameters may help to accelerate drug discovery. American Chemical Society 2022-05-11 /pmc/articles/PMC9134387/ /pubmed/35647436 http://dx.doi.org/10.1021/acsomega.2c00261 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Mamada, Hideaki
Nomura, Yukihiro
Uesawa, Yoshihiro
Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning
title Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning
title_full Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning
title_fullStr Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning
title_full_unstemmed Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning
title_short Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning
title_sort novel qsar approach for a regression model of clearance that combines deepsnap-deep learning and conventional machine learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9134387/
https://www.ncbi.nlm.nih.gov/pubmed/35647436
http://dx.doi.org/10.1021/acsomega.2c00261
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