In the present work, a quantitative structure–property relationship
(QSPR) treatment of temperature of five percent of decomposition (T5) 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 T5 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 T5 of the investigated macromolecules more accurately.