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Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants.
Du, Shanda; Wang, Xiujuan; Wang, Runguo; Lu, Ling; Luo, Yanlong; You, Guohua; Wu, Sizhu.
Afiliación
  • Du S; State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China. wusz@mail.buct.edu.cn.
  • Wang X; Key Laboratory of Rubber-Plastics, Ministry of Education/Shandong Provincial Key Laboratory of Rubber-Plastics, Qingdao University of Science & Technology, Qingdao 266042, China.
  • Wang R; State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China. wusz@mail.buct.edu.cn.
  • Lu L; State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China. wusz@mail.buct.edu.cn.
  • Luo Y; College of Science, Nanjing Forestry University, Nanjing 210037, China.
  • You G; College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China. yough@mail.buct.edu.cn.
  • Wu S; State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China. wusz@mail.buct.edu.cn.
Phys Chem Chem Phys ; 24(21): 13399-13410, 2022 Jun 01.
Article en En | MEDLINE | ID: mdl-35608602
In this study, a total of 302 molecular structures of phenylnaphthylamine antioxidants based on N-phenyl-1-naphthylamine and N-phenyl-2-naphthylamine skeletons with various substituents were modeled by exhaustive methods. Antioxidant parameters, including the hydrogen dissociation energy, solubility parameter, and binding energy, were calculated through molecular simulations. Then, a group decomposition scheme was determined to decompose 302 antioxidants. The antioxidant parameters and decomposition results constituted machine-learning data sets. Using an artificial neural network model, a correlation coefficient between the predicted and true values above 0.88 and an average relative error within 6% were achieved. Random forest models were used to analyze the factors affecting antioxidant activity from chemical and physical perspectives; the results showed that amino and alkyl groups were conducive to improving antioxidant performance. Moreover, substituent positions 1, 7, and 10 of N-phenyl-1-naphthylamine and 3, 7, and 10 of N-phenyl-2-naphthylamine were found to be the optimal positions for modifications to improve antioxidant activity. Two potentially efficient phenylnaphthylamine antioxidant structures were proposed and their antioxidant parameters were also calculated; the hydrogen dissociation energy and solubility parameter decreased by more than 9% and 7%, respectively, whereas the binding energy increased by more than 16% compared with the benchmark of N-phenyl-1-naphthylamine. These results indicate that molecular simulation and machine learning could provide alternative tools for the molecular design of new antioxidants.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático / Antioxidantes Tipo de estudio: Prognostic_studies Idioma: En Revista: Phys Chem Chem Phys Asunto de la revista: BIOFISICA / QUIMICA Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático / Antioxidantes Tipo de estudio: Prognostic_studies Idioma: En Revista: Phys Chem Chem Phys Asunto de la revista: BIOFISICA / QUIMICA Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido