Artificial intelligence for small molecule anticancer drug discovery.

Journal: Expert opinion on drug discovery
PMID: 39074493

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 its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. 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.

Authors

  • Lihui Duo
    Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China.
  • Yu Liu
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China.
  • Jianfeng Ren
    Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China.
  • Bencan Tang
    Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China.
  • Jonathan D Hirst
    School of Chemistry, University of Nottingham, Nottingham, NG7 2RD, United Kingdom.

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