Using support vector regression coupled with the genetic algorithm for predicting acute toxicity to the fathead minnow

Abstract

The potential toxicity of chemicals may present adverse effects to the environment and human health. The quantitative structure–activity relationship (QSAR) provides a useful method for hazard assessment. In this study, we constructed a QSAR model based on a highly heterogeneous data set of 571 compounds from the US Environmental Protection Agency, for predicting acute toxicity to the fathead minnow (Pimephales promelas). An approach coupling support vector regression (SVR) with the genetic algorithm (GA) was developed to build the model. The generated QSAR model showed excellent data fitting and prediction abilities: the squared correlation coefficients (r ²) for the training set and the test set were 0.826 and 0.802, respectively. Only eight critical descriptors, most of which are closely related to the toxicity mechanism, were chosen by GA-SVR, making the derived model readily interpretable. In summary, the successful case reported here highlights that our GA-SVR approach can be used as a general machine learning method for toxicity prediction.

Keywords:

• toxicity prediction
• fathead minnow
• quantitative structure–activity relationship
• support vector regression
• genetic algorithm

Acknowledgements

This work was supported by Hi-TECH Research and Development Program of China (Grants 2006AA10A201, 2006AA020402), National S&T Major Project (Grant 2009ZX09301-001), State Key Program of Basic Research of China (Grant 2009CB918502), and the MOST International Collaboration Program (2007DFB30370).