Prediction of the Q-e parameters from radical structures

To construct reliable quantitative structure-property relationship models of Q and e parameters, density functional theory (DFT) calculations were carried out for 56 radicals with structure (CH3)-H-1-C(2)HR(3)aEuro cent (formed from the monomer + HaEuro cent) at the B3LYP level of theory with 6-31G(d) basis set. The multiple linear regression technique was used to select the descriptors as inputs for the artificial neural network (ANN). A three-descriptor ANN for the parameter lnQ was developed that produced training set root mean square (rms) error = 0.317 and rms error = 0.313 for an external prediction set. A two-descriptor ANN for the parameter e was built with rms errors of 0.264 for the training set and 0.271 for the prediction set. Comparing to existing models, our ANN models show better or comparable statistical characteristics, which shows that calculating quantum chemical descriptors from radicals to develop ANN models and predict lnQ and e values is feasible.