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In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical...

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Autores principales: Yang, Hongbin, Sun, Lixia, Li, Weihua, Liu, Guixia, Tang, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5826228/
https://www.ncbi.nlm.nih.gov/pubmed/29515993
http://dx.doi.org/10.3389/fchem.2018.00030
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author Yang, Hongbin
Sun, Lixia
Li, Weihua
Liu, Guixia
Tang, Yun
author_facet Yang, Hongbin
Sun, Lixia
Li, Weihua
Liu, Guixia
Tang, Yun
author_sort Yang, Hongbin
collection PubMed
description During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.
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spelling pubmed-58262282018-03-07 In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts Yang, Hongbin Sun, Lixia Li, Weihua Liu, Guixia Tang, Yun Front Chem Chemistry During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future. Frontiers Media S.A. 2018-02-20 /pmc/articles/PMC5826228/ /pubmed/29515993 http://dx.doi.org/10.3389/fchem.2018.00030 Text en Copyright © 2018 Yang, Sun, Li, Liu and Tang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Chemistry
Yang, Hongbin
Sun, Lixia
Li, Weihua
Liu, Guixia
Tang, Yun
In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
title In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
title_full In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
title_fullStr In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
title_full_unstemmed In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
title_short In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
title_sort in silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5826228/
https://www.ncbi.nlm.nih.gov/pubmed/29515993
http://dx.doi.org/10.3389/fchem.2018.00030
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