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 inhibitors based on the data set comprising
over 537 reported plant secondary metabolite (PSM) α-glucosidase
inhibitors. We assessed 35 ML models by using seven different fingerprints.
The Random forest with the RDKit fingerprint was the best-performing
model, with an accuracy (ACC) of 83.74% and an area under the ROC
curve (AUC) of 0.803. The resulting robust ML model encompasses all
reported α-glucosidase inhibitory PSMs. The model was employed
to predict potential α-glucosidase inhibitors from an in-house
5810 PSM database. The model identified 965 PSMs with a prediction
activity ≥0.90 for α-glucosidase inhibition. Twenty-four
predicted PSMs were subjected to in vitro assay,
and 13 were found to inhibit α-glucosidase with IC50 ranging from 0.63 to 7 mg/mL. Among them, seven compounds recorded
IC50 values less than the standard drug acarbose and were
investigated further to have optimal drug-likeness and medicinal chemistry
characteristics. The ML model and in vitro experiments
have identified nervonic acid as a promising α-glucosidase inhibitor.
This compound should be further investigated for its potential integration
into the diabetes treatment system.