DPP IV Inhibitors Activities Prediction as An Anti-Diabetic Agent using Particle Swarm Optimization-Support Vector Machine Method

Abstract

Diabetes mellitus is a chronic illness that can affect anyone, while the medicine that can entirely cure diabetes has not been discovered yet. Dipeptidyl Peptidase IV (DPP IV) inhibitor is one of the agents with potency as an anti-diabetic treatment. In this work, we utilized the machine learning method to predict the activity of DPP IV as an anti-diabetic agent. We combined Particle Swarm Optimization (PSO) method for features selection and the Support Vector Machine (SVM) for the prediction model. Three SVM kernels, i.e., radial basis function (RBF), polynomial, and linear, were utilized, and their performance was compared. A Hyperparameter tuning procedure was conducted to improve the performance of models. According to the results, we found that the best model obtained from SVM with RBF kernel with the value R2 of train and test set are 0.79 and 0.85, respectively.

Keywords

• dipeptidyl peptidase iv inhibitor
• particle swarm optimization
• quantitative structure activity relationship
• support vector machine