Application of machine learning in drug side effect prediction: databases, methods, and challenges

Abstract

Drug side effects have become paramount concerns in drug safety research, ranking as the fourth leading cause of mortality following cardiovascular diseases, cancer, and infectious diseases. Simultaneously, the widespread use of multiple prescription and over-the-counter medications by many patients in their daily lives has heightened the occurrence of side effects resulting from Drug-Drug Interactions (DDIs). Traditionally, assessments of drug side effects relied on resource-intensive and time-consuming laboratory experiments. However, recent advancements in bioinformatics and the rapid evolution of artificial intelligence technology have led to the accumulation of extensive biomedical data. Based on this foundation, researchers have developed diverse machine learning methods for discovering and detecting drug side effects. This paper provides a comprehensive overview of recent advancements in predicting drug side effects, encompassing the entire spectrum from biological data acquisition to the development of sophisticated machine learning models. The review commences by elucidating widely recognized datasets and Web servers relevant to the field of drug side effect prediction. Subsequently, The study delves into machine learning methods customized for binary, multi-class, and multi-label classification tasks associated with drug side effects. These methods are applied to a variety of representative computational models designed for identifying side effects induced by single drugs and DDIs. Finally, the review outlines the challenges encountered in predicting drug side effects using machine learning approaches and concludes by illuminating important future research directions in this dynamic field.

Keywords

machine learning / drug side effects / computational models / databases / Web servers

Haochen Zhao received his PhD degree in computer science from Central South University, China. Now He is a Postdoctoral fellow in Computer Science at Central South University, China. His current research interests include bioinformatics and machine learning

Jian Zhong received his MS degree in computer science from Central South University, China. Now He is a PhD candidate in Computer Science at Central South University, China. His current research interests include bioinformatics and machine learning

Xiao Liang is a PhD candidate in School of Computer Science and Engineering at Central South University, China. His current research interests include machine learning and its applications in bioinformatics

Chenliang Xie is an MS student in School of Computer Science and Engineering at Central South University, China. Her current research interests include bioinformatics and machine learning

Shaokai Wang is a PhD candidate in Cheriton School of Vomputer Science, University of Waterloo, Canada. His current research interests include bioinformatics, proteomics and machine learning

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Nos. 62072473, U22A2041), the National Funded Postdoctoral Program of China (No. G2C20233162), and the Natural Science Foundation of Hunan Province (No. 2022JJ30750). This work was carried out in part using computing resources at the High-Performance Computing Center of Central South University.

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

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