# DPPPRED-IV: An Ensembled QSAR-Based Web Server for the Prediction of Dipeptidyl Peptidase 4 Inhibitors

## TL;DR

This paper introduces DPPPRED-IV, a web tool that predicts compounds that inhibit DPP4, a target for treating type 2 diabetes, helping to speed up drug discovery.

## Contribution

The novel contribution is an ensembled QSAR-based web server that combines classification and regression models for DPP4 inhibitor prediction.

## Key findings

- DPPPRED-IV uses genetic algorithm descriptor selection and multiple machine learning algorithms for accurate predictions.
- Experimental testing confirmed 14 out of 29 predicted compounds as significant DPP4 inhibitors at 1.5 µM.
- The tool is integrated into the ChemoPredictionSuite and supports early-stage drug screening for T2DM.

## Abstract

Type 2 diabetes mellitus (T2DM) is a complex and prevalent metabolic disorder, and dipeptidyl peptidase 4 (DPP4) inhibitors have proven effective, yet the identification of novel inhibitors remains challenging due to the vastness of chemical space. In this study, we developed DPPPRED-IV, a web-based ensembled system integrating both binary classification and continuous regression Quantitative Structure Activity Relationships (QSAR) models to predict human DPP4 inhibitory activity. A curated dataset of 4 676 ChEMBL compounds was subjected to genetic algorithm descriptor selection and multiple machine learning algorithms; classification models were combined via a soft voting ensemble, while regression models estimated IC50 values. All models underwent external 10-fold cross-validation and applicability domain analysis. The final models were integrated into a user-friendly web server, allowing predictions from SMILES inputs. Experimental testing of 29 MolPort compounds at 1.5 µM confirmed that 14 predicted actives exhibited significant inhibition, supporting the tool’s performance in early-stage screening. DPPPRED IV is freely available within the ChemoPredictionSuite and offers a resource to accelerate decision making, reduce costs and minimize animal use in T2DM drug discovery.