Journal
JOURNAL OF APPLIED TOXICOLOGY
Volume 41, Issue 10, Pages 1518-1526Publisher
WILEY
DOI: 10.1002/jat.4141
Keywords
applicability domain; computational toxicology; machine learning; mitochondrial toxicity; structural alert
Categories
Funding
- National Natural Science Foundation of China [81673356, 81872800]
- National Key Research and Development Program of China [2019YFA0904800]
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The article discusses the importance of mitochondria in human cells and the potential impact of drugs and chemicals on them, focusing on the development of models that can accurately predict mitochondrial toxicity.
Mitochondria are important organelles in human cells, providing more than 95% of the energy. However, some drugs and environmental chemicals could induce mitochondrial dysfunction, which might cause complex diseases and even worsen the condition of patients with mitochondrial damage. Some drugs have been withdrawn from the market due to their severe mitochondrial toxicity, such as troglitazone. Therefore, there is an urgent need to develop models that could accurately predict the mitochondrial toxicity of chemicals. In this paper, suitable data were obtained from literature and databases first. Then nine types of fingerprints were used to characterize these compounds. Finally, different algorithms were used to build models. Meanwhile, the applicability domain of the prediction models was defined. We have also explored the structural alerts of mitochondrial toxicity, which would be helpful for medicinal chemists to better predict mitochondrial toxicity and further optimize lead compounds.
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