Application of Machine Learning Algorithms in Predicting Drug Efficacy and Toxicity

Abstract

The employment of machine learning algorithms for the purpose of predicting drug efficacy and toxicity has garnered increasing attention in recent times, thereby facilitating drug development processes that are more streamlined and economical. This study aims to explore the application of various machine learning algorithms in predicting the efficacy and toxicity of drugs. The study will begin by collecting a comprehensive dataset consisting of drug properties, chemical structures, molecular descriptors, and corresponding efficacy and toxicity outcomes. The dataset will include information from a diverse range of drugs across multiple therapeutic areas, encompassing both approved and experimental compounds.Predictive models will be created using SVM, random forests, neural networks, and gradient boosting models. Model performance will be assessed using various evaluation metrics such as accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC-ROC). In order to enhance the strength and applicability of the models, meticulous cross-validation and external validation techniques will be implemented. The dataset will be split into training, validation, and test sets to train the models, optimise hyperparameters, and assess their predictive performance on unseen data. This study aims to emphasise the importance of machine learning algorithms in predicting drug efficacy and toxicity. The results of this study will make a significant contribution to the expanding knowledge base in the area of computational drug discovery and establish a useful foundation for forthcoming research and development endeavours.