Machine learning framework for predicting DPP-4 inhibitory activity: a preliminary study on candidate screening and structural determinants

This preliminary study developed a machine learning-based computer-aided drug design framework to predict the inhibitory activity of dipeptidyl peptidase-4 (DPP-4) inhibitors for type 2 diabetes, aiming to streamline the identification of high-potential antidiabetic candidates.The bioactivity data for human DPP-4 were retrieved from ChEMBL, curated, and normalized to the pIC 50 scale.The chemical structures were digitized into 2048-bit Morgan fingerprints.We implemented a dual-model random forest architecture using an 80/20 train-test split to perform qualitative activity classification (threshold pIC 50 6.0)and quantitative potency regression.The model robustness was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) and shapley additive explanations (SHAP) analyses to ensure predictive accuracy and structural interpretability.The random forest classifier achieved superior performance with an ROC-AUC of 0.91, overall accuracy of 91.17%, a precision of 0.9281, and an F1-score of 0.9393.The regression model achieved moderate predictive accuracy suitable for early-stage candidate prioritization.The SHAP analysis identified key structural determinants, notably fingerprint bit 699, as major contributors to the inhibitory activity.In practical screening, the pipeline identified Candidate A as a high-potential lead with a predicted pIC 50 of 6.4 and an 80.0% activity probability.Furthermore, the model validated the drug-likeness of successful candidates using Lipinski's rule of five, ensuring favorable physicochemical properties for oral bioavailability.This integrated quantitative structure-activity relationship framework significantly enhanced drug discovery efficiency by providing accurate qualitative and quantitative predictions.By combining 91% classification accuracy with structural insights, this methodology offers a robust data-driven tool for the accelerated validation of novel DPP-4 inhibitors.