Adjustable QSPRs for prediction of properties of long-chain substances

The development of quantitative structure property relationships (QSPRs) with good extrapolation capabilities for high carbon number (n_C) substances in homologous series is considered. Based on the available experimental data, molecular descriptors collinear with a particular property are identified. Among these, the ones whose behavior at the limit n_C → ∞ is similar to the properties behavior, are eventually used for prediction. A linear QSPR in terms of the selected descriptor with an optional additional correction term which exponentially decays with n_C can be developed. The confidence level in the property prediction can be adjusted to the quantity, precision, and reliability of the available data. The proposed method has been tested by developing QSPRs for predicting T_C and P_C for several homologous series and T_m for the n-alkane series. In all cases, the QSPRs developed represent the available experimental data satisfactorily and converge to theoretically accepted values for n_C → ∞.