The charge and radius of atoms are taken from the Generalized Amber ForceField (GAFF) forcefield (water radius was taken as 1.4 Angstrom). Although, eq. ( ) is valid only for non-overlapping atoms, this can act as an excellent descriptor in an ML model. In the evaluation of eq. ( ), the numerator of the second term can be negative for overlapping accessible surfaces of the atoms. To circumvent this problem, we have performed the sum over the pairs of atoms with non-overlapping accessible surfaces only. This approximation provides a fast estimation of the polar part of the solvation free energy. Our results show that using this approximation as a descriptor, ML-based methods perform well for the FreeSolv database. We have employed four different machine learning models: Random Forest (RF), 47 Extreme Gradient Boosting (XGBoost), Gradient Boosting (GradBoost), and Light Gradient Boosting Machine (LightGBM). These models are trained on the training set to learn the crucial relationships of the descriptors with the HFE which is the target property.