Artificial intelligence for RNA-ligand interaction prediction: advances and prospects.

Journal: Drug discovery today
Published Date: Apr 24, 2025

Abstract

Accurate prediction of RNA-ligand interactions is vital for understanding biological processes and advancing RNA-targeted drug discovery. Given their complexity, artificial intelligence (AI) is revolutionizing the study of RNA-ligand interactions, offering insights into the complex dynamics and therapeutic potential of RNA. In this review, we highlight advances in AI-driven RNA-ligand binding site identification, structure modeling, binding mode and binding affinity prediction, and virtual screening (VS). We also discuss key challenges, such as data set scarcity and modeling RNA flexibility. Future directions emphasize integrating cutting-edge AI techniques with physics-based models and expanding experimental data sets to enhance RNA-ligand interaction predictions.

Authors

  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Yi Tan
    Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China.
  • Ruiqiang Lu
    Ping An Healthcare Technology, Beijing, 100027, China; College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China.
  • Pengyu Liang
    Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Science, Macao Polytechnic University, 999078 Macao, China.
  • Huanxiang Liu
    Lanzhou University.
  • Xiaojun Yao
    Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, PR China.

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