Artificial intelligence for small molecule anticancer drug discovery

Duo, Lihui; Liu, Yu; Ren, Jianfeng; Tang, Bencan; Hirst, Jonathan D.

Abstract

Introduction: The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has several advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges persist, such as low response rates and drug resistance. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships.

Area covered: In this review, we explore the important landmarks in the history of AI-driven drug discovery, highlight various applications in small molecule cancer drug discovery, outline the challenges faced, and provide insights for future research.

Expert opinion: The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.

Citation

Duo, L., Liu, Y., Ren, J., Tang, B., & Hirst, J. D. (2024). Artificial intelligence for small molecule anticancer drug discovery. Expert Opinion on Drug Discovery, 19(8), 933-948. https://doi.org/10.1080/17460441.2024.2367014

• Journal Article Type: Review
• Acceptance Date: Jun 7, 2024
• Online Publication Date: Jun 18, 2024
• Publication Date: Aug 2, 2024
• Deposit Date: Jul 1, 2024
• Publicly Available Date: Jun 19, 2025
• Journal: Expert Opinion on Drug Discovery
• Print ISSN: 1746-0441
• Electronic ISSN: 1746-045X
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 8
• Pages: 933-948
• DOI: https://doi.org/10.1080/17460441.2024.2367014
• Keywords: Drug discovery; machine learning; artificial intelligence; cancer; small molecules
• Public URL: https://nottingham-repository.worktribe.com/output/36580154
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/17460441.2024.2367014
• Additional Information: Peer Review Statement: The publishing and review policy for this title is described in its Aims & Scope.; Aim & Scope: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=iedc20; Received: 2024-04-22; Accepted: 2024-06-07; Published: 2024-06-18