Quantitative Structure–Property Relationship Prediction of Liquid Heat Capacity at 298.15 K for Organic Compounds

Novel QSPR models were developed and evaluated for the
prediction
of heat capacity of liquids at 298.15 K with only three descriptors.
Two linear and nonlinear models were produced using genetic function
approximation (GFA) and adaptive neurofuzzy inference system (ANFIS)
methods based on a data set of 706 compounds with a wide variety of
functional groups. The results showed that both GFA and ANFIS methods
could model the relationship between the liquid heat capacity of organic
compounds and their structures with high accuracy. The predictive
quality of the QSPR models were tested for an external test set, where
the squared correlation coefficients of prediction for the GFA and
ANFIS methods were 0.970 and 0.973, respectively.