Two QSAR models for predicting the toxicity of chemicals towards Tetrahymena pyriformis based on topological-norm descriptors and spatial-norm descriptors

Quantitative structure-activity relationship (QSAR) is important for safe, rapid and effective risk assessment of chemicals. In this study, two QSAR models were established with 1230 chemicals to predict toxicity towards Tetrahymena pyriformis using multiple linear regression (MLR) method. The topological(T)-QSAR model was developed by using topological-norm descriptors generated from the topological structure, and the spatial(S)-QSAR model were built with spatial-norm descriptors obtained from the three-dimensional structure of molecules and topological-norm descriptors. The r(training)(2) and r(test)(2) are 0.8304 and 0.8338 for the T-QSAR model, and 0.8485 and 0.8585 for the S-QSAR model, which means that T-QSAR model and S-QSAR model can be used to predict toxicity quickly and accurately. In addition, we also conducted validation on the developed models. Satisfying validation results and statistical parameters demonstrated that QSAR models based on the topological-norm descriptors and spatial-norm descriptors proposed in this paper could be further utilized to estimate the toxicity of chemicals towards Tetrahymena pyriformis.

Automated summary

In this study, two QSAR models were established using multiple linear regression (MLR) method to predict toxicity towards Tetrahymena pyriformis. The T-QSAR model was developed with topological-norm descriptors generated from the topological structure, while the S-QSAR model utilized spatial-norm descriptors obtained from the three-dimensional structure of molecules and topological-norm descriptors. The r(training)(2) and r(test)(2) values for the T-QSAR model were 0.8304 and 0.8338, and for the S-QSAR model were 0.8485 and 0.8585, indicating their ability to predict toxicity quickly and accurately.