Recent advances in AI-driven protein-ligand interaction predictions

Structure-based drug discovery is a fundamental approach in modern drug development, leveraging computational models to predict protein-ligand interactions. AI-driven methodologies are significantly improving key aspects of the field, including ligand binding site prediction, protein-ligand binding pose estimation, scoring function development, and virtual screening. In this review, we summarize the recent AI-driven advances in various protein-ligand interaction prediction tasks. Traditional docking methods based on empirical scoring functions often lack accuracy, whereas AI models, including graph neural networks, mixture density networks, transformers, and diffusion models, have enhanced predictive…

• SN
  Seoul National University

  Awards: 370C-20220109, 0413-20230053
• NR
  National Research Foundation of Korea

  Awards: RS-2023-00256320, 2022M3E5F3081268, 2022R1C1C1005080, 2020M3A9G7103933
• IF
  Institute for Information and Communications Technology Promotion