Accurate prediction of the solubility parameter of pure compounds from their molecular structures

• Solubility parameter is predicted from the molecular structure of the compounds alone.
• Neural Networks accurately predict the solubility parameter versus linear regression.
• The model employs better-defined and easy to use atom-type molecular group's count.
• The method provides advantages in terms of combined simplicity and accuracy.
• The method is very useful in predicting the solubility potential of various compounds.

A quantitative structure property relation (QSPR) method for predicting the solubility parameter (δ) of pure compounds is presented. Artificial neural network (ANN) model was developed and used to probe the structural groups that have significant contribution to the overall solubility of pure compounds and arrive at the set of groups that can best represent the solubility parameter for about 418 substances. The 36 atom-type structural groups listed can predict the solubility parameter of pure compounds from the knowledge of the molecular structure alone with a correlation coefficient of 0.998 and an absolute standard deviation and error of 0.109 and 0.67%, respectively. The results are further compared with those of the traditional structural group contribution (SGC) method based on multivariable regression as well as other methods in the literature. The method is very useful in predicting the solubility potential of various compounds and has advantages in terms of combined accuracy and simplicity.