Employing Machine Learning Models to Predict Potential α‑Glucosidase Inhibitory Plant Secondary Metabolites Targeting Type‑2 Diabetes and Their In Vitro 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 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.