A graph neural network approach for molecule carcinogenicity prediction.

Journal: Bioinformatics (Oxford, England)
Published Date: Jun 24, 2022

Abstract

MOTIVATION: Molecular carcinogenicity is a preventable cause of cancer, but systematically identifying carcinogenic compounds, which involves performing experiments on animal models, is expensive, time consuming and low throughput. As a result, carcinogenicity information is limited and building data-driven models with good prediction accuracy remains a major challenge.

Authors

  • Philip Fradkin
    Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada.
  • Adamo Young
    Vector Institute, Toronto, ON M5G 1M1, Canada.
  • Lazar Atanackovic
    Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada.
  • Brendan Frey
    Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada.
  • Leo J Lee
    Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Canada. Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada. Program on Genetic Networks and Program on Neural Computation & Adaptive Perception, Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada.
  • Bo Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.

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