While applying the traditional ML models for non-linear regression, we consider SVM, Kernel Ridge, Neural Network, and Random Forest (RF) as reported in Table of the Supplementary Information. We form ten random test-train splits of the data set and calculate mean squared prediction errors (MSE) for each of the four methods above and each split. We then calculate the pooled mean for all ten random splits and compare. Random Forest (RF) produces the least pooled mean prediction error, as shown in Table . Thus, we analyze the feature importance scores measured by the RF model. The next section compares these feature importance values with our proposed machine learning model-based results. Instead of using the default permutation feature importance metric from sklearn , we apply the leave one covariate out (LOCO) algorithm to quantify the feature importance and in Algorithm 1, we illustrate the implementation of LOCO. LOCO is used because implementing permutation importance is computationally expensive for our proposed approach, and we want to keep the feature importance quantification metric the same for both RF and our proposed method. For completeness, we also compared the feature importance scores for the RF method by LOCO with the permutation-based method, and they reflected similar assessments. Figure shows the RF LOCO importance scores for the two subgroups, molecules with < Median MolWt and > Median MolWt respectively. As shown in Figure median molecular weight leads to variations in importance scores. These differences in graphical and chemical feature contributions further motivate an investigation into a possible relationship between them. Nevertheless, importance scores produced by the random forest model are interesting, but unrealistic that only TPSA is significant for <Median MolWt dataset. We thus examine them again applying our proposed machine learning approach that, we believe, is more suitable for this type of data and also, latter transpired being the best model based on a prediction MSE comparison in the next section.