GraphGIM: rethinking molecular graph contrastive learning via geometry image modeling.

Journal: BMC biology
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Learning molecular representations is crucial for accurate drug discovery. Using graphs to represent molecules is a popular solution, and many researchers have used contrastive learning to improve the generalization of molecular graph representations.

Authors

  • Chaoyi Li
    College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China.
  • Hongxin Xiang
    National Pilot School of Software, Yunnan University, Kunming, 650091, China.
  • Wenjie Du
    School of Software Engineering, University of Science and Technology of China, Hefei 230026, China; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China.
  • Tengfei Ma
    Harbin Institute of Technology, Harbin, Heilongjiang Province, China.
  • HaoWen Chen
    College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China.
  • Xiangxiang Zeng
    Department of Computer Science, Hunan University, Changsha, China.
  • Lei Xu
    Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.

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