MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction.

Journal: Bioinformatics (Oxford, England)
PMID:

Abstract

MOTIVATION: During lead compound optimization, it is crucial to identify pathways where a drug-like compound is metabolized. Recently, machine learning-based methods have achieved inspiring progress to predict potential metabolic pathways for drug-like compounds. However, they neglect the knowledge that metabolic pathways are dependent on each other. Moreover, they are inadequate to elucidate why compounds participate in specific pathways.

Authors

  • Bing-Xue Du
    School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.
  • Peng-Cheng Zhao
    School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.
  • Bei Zhu
    School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.
  • Siu-Ming Yiu
    2 Department of Computer Science, The University of Hong Kong , Pokfulam, Hong Kong .
  • Arnold K Nyamabo
    School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China.
  • Hui Yu
    Engineering Technology Research Center of Shanxi Province for Opto-Electric Information and Instrument, Taiyuan 030051, China. 13934603474@nuc.edu.cn.
  • Jian-Yu Shi
    School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China.