GDMol: Generative Double-Masking Self-Supervised Learning for Molecular Property Prediction.

Journal: Molecular informatics
Published Date: Oct 24, 2024

Abstract

BACKGROUND: Effective molecular feature representation is crucial for drug property prediction. Recent years have seen increased attention on graph neural networks (GNNs) that are pre-trained using self-supervised learning techniques, aiming to overcome the scarcity of labeled data in molecular property prediction. Traditional GNNs in self-supervised molecular property prediction typically perform a single masking operation on the nodes and edges of the input molecular graph, masking only local information and insufficient for thorough self-supervised training.

Authors

  • Yingxu Liu
    Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
  • Qing Fan
    Shanghai Mental Health Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
  • Chengcheng Xu
    Intelligent Transportation Research Center, Southeast University, Nanjing, 210096, China. xuchengcheng@seu.edu.cn.
  • Xiangzhen Ning
    Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
  • Yu Wang
    Clinical and Technical Support, Philips Healthcare, Shanghai, China.
  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
  • Yu Xie
    Department of Sociology, Princeton University, Princeton, New Jersey, USA.
  • Yanmin Zhang
    Department of Paediatric Cardiology, Shaanxi Institute for Pediatric Diseases, Affiliate Children's Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Yadong Chen
    Laboratory of Molecular Design and Drug Discovery, School of Science, China; Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198 Jiangsu, China.
  • Haichun Liu
    Laboratory of Molecular Design and Drug Discovery, School of Science, China; Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198 Jiangsu, China.

Keywords

External Resources

Popular Topics
Recent Journals