Application status of traditional computational methods and machine learning in cancer drug repositioning

Abstract

The escalating global burden of cancer has spurred extensive research and development efforts aimed at discovering effective anti-cancer agents. However, the prohibitively high costs associated with developing novel drugs remain a formidable challenge. This paper describes a cost-effective approach, drug repositioning, which repurposes approved drugs for novel therapeutic indications, offering a promising solution to this dilemma. We present a comprehensive review of computational strategies employed in cancer drug repositioning, with a particular focus on machine learning. In recent years, the integration of bioinformatics technologies with multi-omics data has significantly advanced the field of cancer drug repurposing. In particular, machine learning and deep learning techniques have been instrumental in driving substantial progress in this area. This review summarizes the current application of traditional computational methods alongside machine learning in drug repositioning, highlighting the great potential of machine learning, both independently and in synergy with other bioinformatics-based approaches. The insights provided here offer valuable reference for further integration of computational strategies into the research and development of cancer therapies.

Keywords

Cancer / Drug repositioning / Machine learning / Deep learning / Bioinformatics

Declarations

Not applicable.

Authors' contributions

Yixin Cao: Conceptualization, Methodology, Writing - Original draft preparation. Yongzhi Li: Supervision, Writing - Reviewing and Editing. Lingxi Wei, Yan Zhou, Fei Gao: Data curation, Resources, Investigation. Qi Yu: Writing - Reviewing and Editing, Validation.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Funding

This work was supported by grants from the Shanxi Basic Research Program (No.202303021221132), Shanxi Province Science and Technology Innovation Talent Team Special Support(No.202304051001017), and the Innovation and Entrepreneurship Training Program for College Students in Shanxi Province (No. 20240447; No. 20240353).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

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Authors' other information

Not applicable.

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