QSPR prediction of thermal decomposition property of non-vinyl polymers having α-amino acids moieties

In the present work, a quantitative structure-property relationship (QSPR) treatment of temperature of five percent of decomposition (T (5)) of a number of totally 30 optically active polymers was performed by means of a genetic algorithm-based partial least squares (GA-PLS) and artificial neural network (ANN). Suitable set of molecular descriptors were calculated by dragon package and the important descriptors were selected by GA-PLS methods. These descriptors were served as inputs to generate ANN. After optimization and training of the networks, they were used for the calculation of T (5) for the validation set. By comparing of the results obtained from PLS and ANN models, it can be seen that statistical parameters (Fisher ratio, correlation coefficient, and standard error) of the ANN model are better than PLS one, which indicates that nonlinear model can simulate the relationship between the structural descriptors and T (5) of the investigated macromolecules more accurately.