CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning.

Journal: Bioinformatics (Oxford, England)
Published Date: Aug 1, 2023

Abstract

MOTIVATION: Metabolic stability plays a crucial role in the early stages of drug discovery and development. Accurately modeling and predicting molecular metabolic stability has great potential for the efficient screening of drug candidates as well as the optimization of lead compounds. Considering wet-lab experiment is time-consuming, laborious, and expensive, in silico prediction of metabolic stability is an alternative choice. However, few computational methods have been developed to address this task. In addition, it remains a significant challenge to explain key functional groups determining metabolic stability.

Authors

  • Bing-Xue Du
    School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.
  • Yahui Long
    College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410000, China.
  • Xiaoli Li
    State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
  • Min Wu
    Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China.
  • Jian-Yu Shi
    School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China.

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