Abstract
In the present paper, support vector machines (SVMs) are used to develop a quantitative structure-activity relationships (QSAR) model for the reaction rate constants (-logkNO3) of 115 heterogeneous organic compounds, through reaction with nitrate radicals (NO3•) in the troposphere. Two quantum chemical descriptors used as the inputs for the SVM model were calculated with density functional theory (DFT), at the B3LYP level of theory with 6-31G(d) basis set. The best predictions were obtained with the Gaussian radical basis kernel (C = 4, ε =0.15 and γ =3). The average root-mean square (RMS) error for the prediction of kNO3 is 0.502 log units, indicating good robustness and predictive ability. The SVM model, reported here, shows better statistical characteristics compared to existing QSAR models.