The determination of the solubility parameter of organic compounds has been of much significance in the chemical industry. In this study, we propose a predictive method based on the combination of the Group Contribution strategy with the Artificial Neural Network to calculate/estimate the solubility parameter values of about 1620 nonelectrolyte organic compounds at 298.15 K and atmospheric pressure. The chemical functional groups are obtained for various compounds categorized in 81 different chemical families. The final results indicate the following statistical parameters of the presented method: average relative deviation (ARD %) of the determined properties from existing experimental values of 1.5% and a squared correlation coefficient of 0.985. It is finally inferred that the developed model is more accurate and predictive than our previously proposed models based on the Quantitative Structure–Property Relationship algorithm, which yielded 4.6, 3.4, and 3.1 ARD % from experimental values.
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