Improvement in the screening performance of potential aryl hydrocarbon receptor ligands by using supervised machine learning.

Zhu, Kongyang; Shen, Chao; Tang, Chen; Zhou, Yixi; He, Chengyong; Zuo, Zhenghong

Zhu, Kongyang; Shen, Chao; Tang, Chen; Zhou, Yixi; He, Chengyong; Zuo, Zhenghong.

Afiliação

Zhu K; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China.
Shen C; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China.
Tang C; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China.
Zhou Y; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China.
He C; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China. Electronic address: hecy@xmu.edu.cn.
Zuo Z; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, Fujian, 361005, China. Electronic address: zuozhenghong@xmu.edu.cn.

Chemosphere ; 265: 129099, 2021 Feb.

Article em En | MEDLINE | ID: mdl-33272675

Assuntos

Praguicidas; Receptores de Hidrocarboneto Arílico; Ligantes; Aprendizado de Máquina Supervisionado

Palavras-chave

AhR agonists; Deep neural network; Docking; Random forest; Virtual screening

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Praguicidas / Receptores de Hidrocarboneto Arílico Tipo de estudo: Diagnostic_studies / Prognostic_studies / Screening_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

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