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Application of machine learning methods for the development of antidiabetic drugs

Author(s):

Juanjuan Zhao, Pengcheng Xu, Xiujuan Liu, Xiaobo Ji*, Minjie Li, Sooranna Dev, Xiaosheng Qu*, Wencong Lu* and Bing Niu*  

Abstract:


Diabetes is a chronic non-communicable disease caused by several different routes, which has attracted increasing attention. In order to speed up the development of new selective drugs, machine learning (ML) technology has been applied in the process of diabetes drug development, which opens up a new blueprint for drug design. This review provides a comprehensive portrayal of the application of ML in antidiabetic drug use.

Keywords:

antidiabetic drugs, mechanism of action, machine learning, hypoglycemic action, inhibitory activity, DPP-IV inhibitors

Affiliation:

Department of Chemistry, College of Sciences, Shanghai University, 200444, Materials Genome Institute, Shanghai University, Shanghai 200444, Department of Chemistry, College of Sciences, Shanghai University, 200444, Department of Chemistry, College of Sciences, Shanghai University, 200444, Department of Chemistry, College of Sciences, Shanghai University, 200444, Department of Obstetrics and Gynaecology, Imperial College London, Fulham Road, London SW10 9 NH, National Engineering Laboratory of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants, No. 189, Changgang Road, 530023, Nanning, Department of Chemistry, College of Sciences, Shanghai University, 200444, School of Life Sciences, Shanghai University, 200444



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