In order to ensure the reproducibility and sharing of the developed model, the selection of descriptor packages and machine learning approaches was limited to those available in open-source openOCHEM. To eliminate redundancy, unsupervised filtering was employed by removing descriptors with less than two different values per training dataset and those with strong correlation (Pearson correlation > 0.95). The number of descriptors for each package after filtering is presented in Supplementary Table . Based on previous studies indicating that the use of 3D descriptors may not significantly improve solubility prediction, we focused on 2D descriptors. Following preliminary analysis, we selected nine descriptor packages to build our models. These packages are briefly described below.