Approach to estimation and prediction for normal boiling point (NBP) of alkanes based on a novel molecular distance-edge (MDE) vector, λ

Models that estimate and predict the normal boiling point (NBP) of alkanes based on a molecular distance-edge (MDE) vector, lambda, have been developed by using multiple linear regression (MLR) methods. The structures of the examined compounds are selectively described by an MDE vector structure descriptor, a novel molecular distance-edge vector recently developed in our laboratory. MLR was used to develop a Linear model containing ten variables with a high precision root mean squares error (RMS = 4.985K) and a good correlation with the correlation coefficient (R = 0.9948). In addition, a predictive model has been developed by using 125 isomers in alkanes as the training set, and its performance was certified by employing 25 alkanes chosen randomly as the test set from a total of 150 alkane compounds; excellent predicted results were obtained with the RMS and R values found between the calculated value and observed NBP being RMS = 4.486K and R = 0.9945.