Quantitative structure-activity relationship models for prediction of sensory irritants (log RD50) of volatile organic chemicals

Quantitative classification and regression models for prediction of sensory irritants (log RD50) of volatile organic chemicals (VOCs) have been developed. Each compound was represented by the calculated structural descriptors to encode constitutional, topological, geometrical, electrostatic, and quantum–chemical features. The heuristic method (HM) was then used to search the descriptor space and select the descriptors responsible for activity. The best classification results were found using support vector machine (SVM): the accuracy for training, test and overall data set is 96.5%, 85.7% and 94.4%, respectively. The nonlinear regression models were built by radial basis function neural networks (RNFNN) and SVM, respectively. The root mean squared errors (RMS) in prediction for the training, test and overall data set are 0.4755, 0.6322 and 0.5009 for reactive group, 0.2430, 0.4798 and 0.3064 for nonreactive group by RBFNN. The comparative results obtained by SVM are 0.4415, 0.7430 and 0.5140 for reactive group, 0.3920, 0.4520 and 0.4050 for nonreactive group, respectively. This paper proposes an effective method for poisonous chemicals screening and considering.