Applying Chaotic Particle Swarm Optimization for the Prediction of Drug-to-Drug Interactions

Abstract

The prediction of drug-to-drug interactions (DDIs) is a critical aspect of pharmacology, with significant implications for patient safety and therapeutic efficacy. This paper proposes a novel approach using Chaotic Particle Swarm Optimization (CPSO) to enhance the prediction accuracy of potential DDIs. The CPSO algorithm, known for its ability to escape local optima, is applied to feature selection and optimization in DDI prediction models.