Modeling Structure-Activity Relationships with Machine Learning to Identify DPP4 Inhibitors as potential Therapeutics for Type 2 Diabetes

Abstract

The development of potent dipeptidyl peptidase-4 (DPP4) inhibitors remains a promising therapeutic strategy for the management of type 2 diabetes mellitus (T2DM). In the present study, an integrated computational workflow incorporating machine learning-based quantitative structure-activity relationship (QSAR) modeling, ligand-based virtual screening, molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations was employed to identify novel DPP4 inhibitors. A curated dataset of experimentally validated DPP4 inhibitors was obtained from the ChEMBL database and subjected to systematic preprocessing and molecular descriptor generation. Several machine learning regression algorithms were initially evaluated to identify the most suitable predictive models. The best-performing tree-based algorithms were subsequently optimized and combined using Ridge Stacking and Weighted Average ensemble strategies. Among the developed models, the optimized Ridge Stacking ensemble demonstrated the highest predictive performance, achieving an R 2 of 0.746, an RMSE of 0.819, and a Pearson correlation coefficient of 0.864, indicating strong predictive accuracy and good generalization capability. The robustness of the model was further confirmed through 10-fold cross-validation, bootstrap validation, residual analysis, and applicability domain assessment. The validated ensemble model was then used to screen 95 compounds identified through ligand-based virtual screening. Among these candidates, CP20 exhibited the highest predicted pIC 50 value and was selected for further evaluation together with the reference inhibitor omarigliptin. Molecular docking, structural interaction fingerprinting, molecular dynamics simulations, and MM/GBSA and MM/PBSA binding free energy analyses demonstrated that CP20 formed stable interactions with key catalytic residues of DPP4 and maintained favorable conformational stability throughout the simulation. Collectively, these findings identify CP20 as a promising lead scaffold for the development of novel DPP4 inhibitors and demonstrate the effectiveness of an ensemble machine learning-guided computational framework for accelerating antidiabetic drug discovery. Experimental validation is warranted to confirm its biological activity and therapeutic potential.