Machine Learning Models for Predicting Drug Efficacy and Side Effects: Developing machine learning models to predict drug efficacy and potential side effects based on patient characteristics and pharmacogenomic data, aiding in treatment selection and pers

Abstract

This paper presents a comprehensive review and analysis of machine learning (ML) models for predicting drug efficacy and side effects. The rapid advancement of ML techniques, coupled with the availability of large-scale pharmacogenomic and patient data, has enabled the development of predictive models that can assist in treatment selection and personalized medicine. We discuss various ML algorithms, data sources, and feature selection techniques used in this domain. Additionally, we highlight challenges, such as data heterogeneity and model interpretability, and propose future research directions to enhance the accuracy and usability of these models.