Prediction of Heat Capacities of Hydration of Various Organic Compounds Using Partial Least Squares and Artificial Neural Network

A quantitative structure–property relationship study based on artificial neural network (ANN) was carried out for the prediction of the heat capacities of hydration of a set of 289 organic compounds of very different chemical natures. The genetic algorithm-partial least squares (GA-PLS) method was applied as a variable selection tool. A PLS method was used to select the best descriptors, and the selected descriptors were then used as input neurons in a neural network model. These descriptors are: number of H atoms (NHA), maximum partial charge in the molecule (Qmax), atomic charge weighted PPSA (PPSA3), relative positive charge (RPCG), minimum net atomic charge (Qmin), fractional PPSA (FPSA3), and Randic index (order 1) (1χ). The results obtained show the ability of the developed artificial neural network model to predict heat capacities of hydration of various organic compounds. Also, the results reveal the superiority of the ANN over the PLS model.