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AI-Driven Advances in Protein–Ligand Docking

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2025, 30(11), pp.223~234
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 29, 2025
  • Accepted : November 10, 2025
  • Published : November 28, 2025

Jae Hyun Kim 1 Chan-Hyuk Kwon 2 Sumi Lee 3 Min Woo Ha 1

1제주대학교
2서울신길재활의학과의원
3전북대학교

Accredited

ABSTRACT

In this paper, we review AI-based protein-ligand docking, highlighting its evolution from traditional techniques to modern approaches utilizing deep learning and diffusion models. Computer-Aided Drug Design (CADD) accelerates discovery, with docking central to pose prediction and virtual screening. Conventional workflows split pose sampling (GA/MC/MD) and scoring (force-field/empirical/knowledge-based), but suffer from receptor rigidity and binding-site dependence. AI mitigates these via CNN/GNN rescoring, learned site prediction, and generative models. Diffusion docking (DiffDock) denoises translation/rotation/torsions, boosting top-k accuracy. An EGFR-Gefitinib study (1M17) contrasts AutoDock Vina, GNINA, and DiffDock, motivating hybrid AI-physics pipelines.

Citation status

* References for papers published after 2024 are currently being built.

This paper was written with support from the National Research Foundation of Korea.