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Nowadays, quantitative structure⁻activity relationship (QSAR) methods have been widely performed to predict the toxicity of compounds to organisms due to their simplicity, ease of implementation, and low hazards. In this study, to estimate the toxicities of substituted aromatic compounds to Tetrahymena pyriformis , the QSAR models were established by the multiple linear regression (MLR) and radial basis function neural network (RBFNN). Unlike other QSAR studies, according to the difference of functional groups (−NO₂, −X), the whole dataset was divided into three groups and further modeled separately. The statistical characteristics for the models are obtained as the following: MLR: n = 36, R² = 0.829, RMS (root mean square) = 0.192, RBFNN: n = 36, R² = 0.843, RMS = 0.167 for Group 1; MLR: n = 60, R² = 0.803, RMS = 0.222, RBFNN: n = 60, R² = 0.821, RMS = 0.193 for Group 2; MLR: n = 31 R² = 0.852, RMS = 0.192; RBFNN: n = 31, R² = 0.885, RMS = 0.163 for Group 3, respectively. The results were within the acceptable range, and the models were found to be statistically robust with high external predictivity. Moreover, the models also gave some insight on those characteristics of the structures that most affect the toxicity.

Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach