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 (r2) 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.