Computational approaches for predicting drug-disease associations: a comprehensive review

Abstract

In recent decades, traditional drug research and development have been facing challenges such as high cost, long timelines, and high risks. To address these issues, many computational approaches have been proposed for predicting the relationship between drugs and diseases through drug repositioning, aiming to reduce the cost, development cycle and risks associated with developing new drugs. Researchers have explored different computational methods to predict drug-disease associations, including drug side effects-disease associations, drug-target associations, and miRNA-disease associations. In this comprehensive review, we focus on recent advances in predicting drug-disease association methods for drug repositioning. We first categorize these methods into several groups, including neural network-based algorithms, matrix-based algorithms, recommendation algorithms, link-based reasoning algorithms, and text mining and semantic reasoning. Then, we compare the prediction performance of existing drug-disease association prediction algorithms. Lastly, we discuss the current challenges and future perspectives in the field of drug-disease associations.

Graphical abstract

Keywords

drug-disease association / association prediction / drug repositioning / machine learning

Zhaoyang Huang is currently pursuing a doctoral degree at the School of Computer Science and Technology, Xidian University, China. His research endeavors focus on the analysis of single-cell RNA sequencing (scRNA-seq) data, the development of algorithms for predicting cellular differentiation, and the exploration of novel applications for existing drugs through drug repositioning strategies

Zhichao Xiao is currently pursuing a master’s degree in the School of Computer Science and Technology at Xidian University, China. His main research interests are in bioinformatics and fuzzy theory

Chunyan Ao received the PhD degree from the School of Computer Science and Technology, Xidian University, China in 2024. Her research interests are bioinformatics and machine learning

Lixin Guan graduated from the School of Computer Science and Technology of Xidian University, China in 2022 with a master’s degree. Her research interests are computational bioinformatics and association prediction

Liang Yu is a professor and doctoral supervisor at the School of Computer Science and Technology, Xidian University, China. She obtained her PhD degree in engineering from Xidian University, China in June 2011. She visited Harvard Medical School, USA from September 2017 to September 2018. She has led two general projects funded by the National Natural Science Foundation of China, one project from the Youth Fund, and one project from the Natural Science Foundation of Shaanxi Province. As a key participant, she has contributed to two major projects supported by the National Natural Science Foundation of China, one major research project, and one general project. She has published over 60 academic papers in this field as the first or corresponding author

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62072353, 62272065, and 62202081).

Competing interests

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

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