Employing Machine Learning Models to Predict Potential α‑Glucosidase Inhibitory Plant Secondary Metabolites Targeting Type‑2 Diabetes and Their <i>In Vitro</i> Validation

The need for new antidiabetic drugs is evident, considering the ongoing global burden of type-2 diabetes mellitus despite notable progress in drug discovery from laboratory research to clinical application. This study aimed to build machine learning (ML) models to predict potential α-glucosidase inh...

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Bibliographic Details
Main Author:	Lemnaro Jamir (19774782) (author)
Other Authors:	Hariprasad P. (14250659) (author)
Published:	2024
Subjects:	Biochemistry Medicine Pharmacology Biotechnology Cancer Virology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified ongoing global burden new antidiabetic drugs medicinal chemistry characteristics identified nervonic acid four predicted psms diabetes treatment system data set comprising build machine learning standard drug acarbose glucosidase inhibitory psms glucosidase inhibitors based predict potential α 50 </ sub glucosidase inhibitors vitro </ potential integration optimal drug drug discovery glucosidase inhibitor glucosidase inhibition values less study aimed roc curve reported α rdkit fingerprint random forest promising α laboratory research inhibit α clinical application 7 mg
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Employing Machine Learning Models to Predict Potential α‑Glucosidase Inhibitory Plant Secondary Metabolites Targeting Type‑2 Diabetes and Their <i>In Vitro</i> Validation

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