Neural Architecture Search-Driven Optimization of Deep Learning Models for Drug Response Prediction

Abstract

In this study, the efficacy of various Neural Architecture Search (NAS) techniques for optimizing neural network architectures in drug response prediction is explored. Accurate prediction of drug responses is crucial for advancing personalized medicine, enabling personalized therapeutic interventions that enhance effectiveness and reduce adverse effects. Traditional models often rely on manually designed architectures, which may not fully capture the complex relationships among drug properties, genetic variations, and cellular phenotypes. An automated NAS approach is introduced to optimize neural network architectures for drug response prediction. The framework explores a defined search space using three techniques: Random Search, Q-Learning, and Bayesian Optimization. A modular architecture that integrates layers, activation functions, and dropout rates is proposed. Findings reveal the strengths and limitations of each NAS method, offering insights into effective model optimization strategies. Validation on publicly available pharmacogenomics datasets shows that NAS-optimized models outperform conventional deep learning and machine learning approaches, highlighting the potential of NAS to enhance predictive modelling in drug response and support personalized medicine and drug development.