Predicting chemical toxicity effects based on chemical-chemical interactions.

Toxicity is a major contributor to high attrition rates of new chemical entities in drug discoveries. In this study, an order-classifier was built to predict a series of toxic effects based on data concerning chemical-chemical interactions under the assumption that interactive compounds are more lik...

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Autores principales:	Lei Chen, Jing Lu, Jian Zhang, Kai-Rui Feng, Ming-Yue Zheng, Yu-Dong Cai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/59f7103666444374bf87f60613f96f77
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spelling	oai:doaj.org-article:59f7103666444374bf87f60613f96f772021-11-18T07:57:18ZPredicting chemical toxicity effects based on chemical-chemical interactions.1932-620310.1371/journal.pone.0056517https://doaj.org/article/59f7103666444374bf87f60613f96f772013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23457578/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Toxicity is a major contributor to high attrition rates of new chemical entities in drug discoveries. In this study, an order-classifier was built to predict a series of toxic effects based on data concerning chemical-chemical interactions under the assumption that interactive compounds are more likely to share similar toxicity profiles. According to their interaction confidence scores, the order from the most likely toxicity to the least was obtained for each compound. Ten test groups, each of them containing one training dataset and one test dataset, were constructed from a benchmark dataset consisting of 17,233 compounds. By a Jackknife test on each of these test groups, the 1(st) order prediction accuracies of the training dataset and the test dataset were all approximately 79.50%, substantially higher than the rate of 25.43% achieved by random guesses. Encouraged by the promising results, we expect that our method will become a useful tool in screening out drugs with high toxicity.Lei ChenJing LuJian ZhangKai-Rui FengMing-Yue ZhengYu-Dong CaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 2, p e56517 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lei Chen Jing Lu Jian Zhang Kai-Rui Feng Ming-Yue Zheng Yu-Dong Cai Predicting chemical toxicity effects based on chemical-chemical interactions.
description	Toxicity is a major contributor to high attrition rates of new chemical entities in drug discoveries. In this study, an order-classifier was built to predict a series of toxic effects based on data concerning chemical-chemical interactions under the assumption that interactive compounds are more likely to share similar toxicity profiles. According to their interaction confidence scores, the order from the most likely toxicity to the least was obtained for each compound. Ten test groups, each of them containing one training dataset and one test dataset, were constructed from a benchmark dataset consisting of 17,233 compounds. By a Jackknife test on each of these test groups, the 1(st) order prediction accuracies of the training dataset and the test dataset were all approximately 79.50%, substantially higher than the rate of 25.43% achieved by random guesses. Encouraged by the promising results, we expect that our method will become a useful tool in screening out drugs with high toxicity.
format	article
author	Lei Chen Jing Lu Jian Zhang Kai-Rui Feng Ming-Yue Zheng Yu-Dong Cai
author_facet	Lei Chen Jing Lu Jian Zhang Kai-Rui Feng Ming-Yue Zheng Yu-Dong Cai
author_sort	Lei Chen
title	Predicting chemical toxicity effects based on chemical-chemical interactions.
title_short	Predicting chemical toxicity effects based on chemical-chemical interactions.
title_full	Predicting chemical toxicity effects based on chemical-chemical interactions.
title_fullStr	Predicting chemical toxicity effects based on chemical-chemical interactions.
title_full_unstemmed	Predicting chemical toxicity effects based on chemical-chemical interactions.
title_sort	predicting chemical toxicity effects based on chemical-chemical interactions.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/59f7103666444374bf87f60613f96f77
work_keys_str_mv	AT leichen predictingchemicaltoxicityeffectsbasedonchemicalchemicalinteractions AT jinglu predictingchemicaltoxicityeffectsbasedonchemicalchemicalinteractions AT jianzhang predictingchemicaltoxicityeffectsbasedonchemicalchemicalinteractions AT kairuifeng predictingchemicaltoxicityeffectsbasedonchemicalchemicalinteractions AT mingyuezheng predictingchemicaltoxicityeffectsbasedonchemicalchemicalinteractions AT yudongcai predictingchemicaltoxicityeffectsbasedonchemicalchemicalinteractions
_version_	1718422731037343744

Predicting chemical toxicity effects based on chemical-chemical interactions.

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