Straightforward MIA‐QSTR evaluation of environmental toxicities of aromatic aldehydes to Tetrahymena pyriformis

Aldehydes are toxic environmental contaminants which cause severe health hazards. There is a growing need by industries and regulatory agencies for the development of tools able to assess the potential hazardous effects of chemicals on living organisms. In this background, multivariate image analysis combined with quantitative structure–toxicity relationships (MIA-QSTR) was used to evaluate the toxicity of aromatic aldehydes to Tetrahymena pyriformis. The techniques of genetic algorithm–partial least squares (GA-PLS) were applied effectively as MIA descriptor selection and mapping tools. In MIA-QSTR evaluation, pixels of 2D images of chemical structures could be used to recognize physicochemical information and predict changes in the toxicities. The resulting MIA-QSTR explains 90.3% leave-one-out predicted variance and 93.1% external predicted variance. The MIA-QSTR/GA-PLS performances were validated using various evaluation techniques such as cross-validation, applicability domain and Y-scrambling procedures, suggesting that the present methodology together with mechanistic interpretation may be useful to evaluate toxicity, safety and risk assessment of toxic environmental contaminants.