Open AccessA graph neural network approach for molecule carcinogenicity prediction
Author(s) -
Philip Fradkin,
Adamo Young,
Lazar Atanackovic,
Brendan J. Frey,
Leo J. Lee,
Bo Wang
Publication year - 2022
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btac266
Subject(s) - computer science , machine learning , artificial intelligence , data mining , source code , graph , theoretical computer science , programming language
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.