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Developing a GNN-based AI model to predict mitochondrial toxicity using the bagging method.
Igarashi, Yoshinobu; Kojima, Ryosuke; Matsumoto, Shigeyuki; Iwata, Hiroaki; Okuno, Yasushi; Yamada, Hiroshi.
Afiliação
  • Igarashi Y; Toxicogenomics Informatics Project, National Institutes of Biomedical Innovation, Health and Nutrition.
  • Kojima R; Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University.
  • Matsumoto S; Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University.
  • Iwata H; Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University.
  • Okuno Y; Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University.
  • Yamada H; Toxicogenomics Informatics Project, National Institutes of Biomedical Innovation, Health and Nutrition.
J Toxicol Sci ; 49(3): 117-126, 2024.
Article em En | MEDLINE | ID: mdl-38432954
ABSTRACT
Mitochondrial toxicity has been implicated in the development of various toxicities, including hepatotoxicity. Therefore, mitochondrial toxicity has become a major screening factor in the early discovery phase of drug development. Several models have been developed to predict mitochondrial toxicity based on chemical structures. However, they only provide a binary classification of positive or negative results and do not provide the substructures that contribute to a positive decision. Therefore, we developed an artificial intelligence (AI) model to predict mitochondrial toxicity and visualize structural alerts. To construct the model, we used the open-source software library kMoL, which employs a graph neural network approach that allows learning from chemical structure data. We also utilized the integrated gradient method, which enables the visualization of substructures that contribute to positive results. The dataset used to construct the AI model exhibited a significant imbalance, with significantly more negative than positive data. To address this, we employed the bagging method, which resulted in a model with high predictive performance, as evidenced by an F1 score of 0.839. This model can also be used to visualize substructures that contribute to mitochondrial toxicity using the integrated gradient method. Our AI model predicts mitochondrial toxicity based on chemical structures and may contribute to screening mitochondrial toxicity in the early stages of drug discovery.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Desenvolvimento de Medicamentos Idioma: En Revista: J Toxicol Sci Ano de publicação: 2024 Tipo de documento: Article País de publicação: Japão

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Desenvolvimento de Medicamentos Idioma: En Revista: J Toxicol Sci Ano de publicação: 2024 Tipo de documento: Article País de publicação: Japão