XGBoost uses regularization, parallel processing, and tree-trimming methods to overcome the over-fitting. LightGBM, another variant of Gradient Boosting, was developed to handle large amounts of data, it uses leaf-wise growth and histogram-based learning to speed up and reduce memory usage. Collectively, these models use various techniques to merge decision trees to balance the prediction accuracy and computing time. We have trained and tested our ML models on two classes of data: 1) full data set, and 2) without outliers. We have used the interquartile range (IQR) method to define outliers in the experimental hydration-free energy, with bounds set at Q 1 -1.5 * IQR and Q 3 + 1.5 * IQR. Q1 and Q3 are the first and third quartiles, respectively. This leaves 628 number of molecules in the second dataset set.