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SAR and QSAR in Environmental Research Volume 32, 2021 - Issue 2
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Research Article

SAR and QSAR research on tyrosinase inhibitors using machine learning methods

Y. Wua State Key Laboratory of Chemical Resource Engineering Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. ChinaView further author information
,
D. Huoa State Key Laboratory of Chemical Resource Engineering Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. ChinaView further author information
,
G. Chenb College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaView further author information
&
A. Yana State Key Laboratory of Chemical Resource Engineering Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. ChinaCorrespondence[email protected]
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Pages 85-110 | Received 11 Oct 2020, Accepted 07 Dec 2020, Published online: 01 Feb 2021

Citations (9)

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Read on this site (4)

J. Devillers, V. Sartor & H. Devillers. (2022) Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models. SAR and QSAR in Environmental Research 33:9, pages 729-751.
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Y. Ji, R. Li, Y. Tian, G. Chen & A. Yan. (2022) Classification models and SAR analysis on thromboxane A2 synthase inhibitors by machine learning methods. SAR and QSAR in Environmental Research 33:6, pages 429-462.
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J. Devillers, V. Sartor, J.P. Doucet, A. Doucet-Panaye & H. Devillers. (2022) In silico prediction of mosquito repellents for clothing application. SAR and QSAR in Environmental Research 33:4, pages 239-257.
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S. Nandi, P. Kumar, S.A. Amin, T. Jha & S. Gayen. (2021) First molecular modelling report on tri-substituted pyrazolines as phosphodiesterase 5 (PDE5) inhibitors through classical and machine learning based multi-QSAR analysis. SAR and QSAR in Environmental Research 32:11, pages 917-939.
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Articles from other publishers (5)

Jianbo Tong, Peng Gao, Haiyin Xu & Yuan Liu. (2024) Improved SAR and QSAR models of SARS-CoV-2 Mpro inhibitors based on machine learning. Journal of Molecular Liquids 394, pages 123708.
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Xiaoyang Pang, Yunyang Zhao, Guo Li, Jianrong Liu & Aixia Yan. (2023) A SAR and QSAR study on cyclin dependent kinase 4 inhibitors using machine learning methods. Digital Discovery 2:4, pages 1026-1041.
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Xiaopeng Yang, Zhenyu Zhao, Chenhui Zhao, Yan Li, Attalla F. El-kott & Mutasem Z. Bani-Fwaz. (2022) Anti-breast Adenocarcinoma and Anti-urease Anti-tyrosinase Properties of 5-Pentylresorcinol as Natural Compound with Molecular Docking Studies. Journal of Oleo Science 71:7, pages 1031-1038.
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Jingbo Yang, Denan Zhang, Yiyang Cai, Kexin Yu, Mingming Li, Lei Liu & Xiujie Chen. (2022) Computational prediction of drug phenotypiceffects based on substructure-phenotype associations. IEEE/ACM Transactions on Computational Biology and Bioinformatics, pages 1-1.
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Ercan Çınar, Eyüp Başaran, Ömer Erdoğan, Reşit Çakmak, Mehmet Boğa & Özge Çevik. (2021) Heterocyclic Schiff base derivatives containing pyrazolone moiety: Synthesis, characterization, and in vitro biological studies. Journal of the Chinese Chemical Society 68:12, pages 2355-2367.
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