Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II

Abstract Assessing the hERG liability in the early stages of drug discovery programs is important. The recent increase of hERG-related information in public databases enabled various successful applications of machine learning techniques to predict hERG inhibition. However, most of these researches...

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Autores principales:	Keiji Ogura, Tomohiro Sato, Hitomi Yuki, Teruki Honma
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/095e0068fd624717acf73f01c55aecca
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spelling	oai:doaj.org-article:095e0068fd624717acf73f01c55aecca2021-12-02T15:08:30ZSupport Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II10.1038/s41598-019-47536-32045-2322https://doaj.org/article/095e0068fd624717acf73f01c55aecca2019-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-47536-3https://doaj.org/toc/2045-2322Abstract Assessing the hERG liability in the early stages of drug discovery programs is important. The recent increase of hERG-related information in public databases enabled various successful applications of machine learning techniques to predict hERG inhibition. However, most of these researches constructed the datasets from only one database, limiting the predictability and scope of the models. In this study, a hERG classification model was constructed using the largest dataset for hERG inhibition built by integrating multiple databases. The integrated dataset consisted of more than 291,000 structurally diverse compounds derived from ChEMBL, GOSTAR, PubChem, and hERGCentral. The prediction model was built by support vector machine (SVM) with descriptor selection based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize the descriptor set for maximum prediction performance with the minimal number of descriptors. The SVM classification model using 72 selected descriptors and ECFP_4 structural fingerprints recorded kappa statistics of 0.733 and accuracy of 0.984 for the test set, substantially outperforming the prediction performance of the current commercial applications for hERG prediction. Finally, the applicability domain of the prediction model was assessed based on the molecular similarity between the training set and test set compounds.Keiji OguraTomohiro SatoHitomi YukiTeruki HonmaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-12 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Keiji Ogura Tomohiro Sato Hitomi Yuki Teruki Honma Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
description	Abstract Assessing the hERG liability in the early stages of drug discovery programs is important. The recent increase of hERG-related information in public databases enabled various successful applications of machine learning techniques to predict hERG inhibition. However, most of these researches constructed the datasets from only one database, limiting the predictability and scope of the models. In this study, a hERG classification model was constructed using the largest dataset for hERG inhibition built by integrating multiple databases. The integrated dataset consisted of more than 291,000 structurally diverse compounds derived from ChEMBL, GOSTAR, PubChem, and hERGCentral. The prediction model was built by support vector machine (SVM) with descriptor selection based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize the descriptor set for maximum prediction performance with the minimal number of descriptors. The SVM classification model using 72 selected descriptors and ECFP_4 structural fingerprints recorded kappa statistics of 0.733 and accuracy of 0.984 for the test set, substantially outperforming the prediction performance of the current commercial applications for hERG prediction. Finally, the applicability domain of the prediction model was assessed based on the molecular similarity between the training set and test set compounds.
format	article
author	Keiji Ogura Tomohiro Sato Hitomi Yuki Teruki Honma
author_facet	Keiji Ogura Tomohiro Sato Hitomi Yuki Teruki Honma
author_sort	Keiji Ogura
title	Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
title_short	Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
title_full	Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
title_fullStr	Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
title_full_unstemmed	Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
title_sort	support vector machine model for herg inhibitory activities based on the integrated herg database using descriptor selection by nsga-ii
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/095e0068fd624717acf73f01c55aecca
work_keys_str_mv	AT keijiogura supportvectormachinemodelforherginhibitoryactivitiesbasedontheintegratedhergdatabaseusingdescriptorselectionbynsgaii AT tomohirosato supportvectormachinemodelforherginhibitoryactivitiesbasedontheintegratedhergdatabaseusingdescriptorselectionbynsgaii AT hitomiyuki supportvectormachinemodelforherginhibitoryactivitiesbasedontheintegratedhergdatabaseusingdescriptorselectionbynsgaii AT terukihonma supportvectormachinemodelforherginhibitoryactivitiesbasedontheintegratedhergdatabaseusingdescriptorselectionbynsgaii
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Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II

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