EM-PLA: Environment-aware Heterogeneous Graph-based Multimodal Protein-Ligand Binding Affinity Prediction.

Journal: Bioinformatics (Oxford, England)
Published Date: May 12, 2025

Abstract

MOTIVATION: Predicting protein-ligand binding affinity accurately and quickly is a major challenge in drug discovery. Recent advancements suggest that deep learning-based computational methods can effectively quantify binding affinity, making them a promising alternative. Environmental factors significantly influence the interactions between protein pockets and ligands, affecting the binding strength. However, many existing deep learning approaches tend to overlook these environmental effects, focusing instead on extracting features from proteins and ligands based solely on their sequences or structures.

Authors

  • Zhiqi Xie
    College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China.
  • Peng Zhang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.
  • Zipeng Fan
    College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China.
  • Qingpeng Zhang
    Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, Hong Kong SAR, China.
  • Qianxi Lin
    College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China.

Keywords

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