Therefore, the focus of the model performance evaluation across different sets of features-MACCS, GC, and GC-MACCS-is on the LGBM model. All numerical data for training, nested five-fold cross validation (CV), and testing for the aqueous solubility and organic solubility models are given in Table and Table . The hyperparameters used for training the models are reported in Table and Table . Overall, the LGBM model trained on GC-MACCS features is our best-performing model for both aqueous solubility and organic solubility predictions, as shown in the parity plots in Fig. . The model achieved the highest R² score and the lowest RMSE and MAE scores. For aqueous solubility, the model trained on GC-MACCS achieved a five-fold CV R 2 score of 0.863 and a test score of 0.864 (Fig. ). This test score of 0.864 is comparable to the previous studies that trained ML models on the AqSolDB dataset. In the case of the organic solubility, a direct comparison with the previous studies is not possible because of differences in datasets and data pre-processing. Nonetheless, the herein reported nested five-fold CV R 2 score of 0.787 and a test R 2 score of 0.805 (Fig. ) is comparable to previous studies. Case study of the organic solubility trends Although the models achieved satisfactory results based on quantitative performance metrics, it is important to evaluate directly if the models can make appropriate predictions for previously unseen solutes. Vassileiou and coworkers observed a performance drop from R 2 = 0.78 for ten-fold CV for single solute-solvent pairs to R 2 = 0.56 for the Leave-One-Solute-Out CV when predicting organic solubility. Such an observation highlights the potential issues with the generalizability of the models for organic solubility. As previously stated, some of these issues can be attributed to the experimental uncertainty of solubility measurements, where the standard deviation can reach 0.5 log(S / mol/dm³). Another potential issue is the uneven distribution of data points across solvents in BigSolDB as both the number of measurements per solvent and the diversity of solutes in each solvent vary substantially. Consequently, the development of quantitatively accurate organic solubility models is challenging. However, we hypothesize that a ML model should be capable of capturing the solubility trends, that is, being able to rank the solvents from best to worst. Thirty molecules were selected for a case study analysis from BigSolDB. These molecules were chosen through stratified sampling, which was based on the log(P) (logarithm of the octanolwater partition coefficient) and aliphatic ratio of the molecules to ensure a representative distribution of lipophilicity and aromaticity among the molecules. Because our experimental focus is on small molecules, before sampling we filter large molecules with the number of non-hydrogen atoms exceeding fifteen. Additionally, only molecules with at least seven solvents were included to ensure our case study molecules span a diverse set of solvents. Stratified sampling was performed using a total of 16 two-dimensional bins, formed by combining four bins for log(P) and four bins for the aliphatic ratio. The number of bins was determined based on Sturges' rule. We note that the number of solvents available for each molecule varies, as this is inherent to the BigSolDB dataset. The molecules for the case study were removed from the dataset prior to training the model to avoid data leakage. For each case study molecule, a new LGBM model is trained on GC-MACCS features to test its generalization performance. Two metrics are used to assess the performance of the model. RMSE is used to determine how far the solubility prediction is from the experimental values. Kendall's Tau is used to quantify the ability of the model to accurately rank the solvents from best to worst. The value range of this metric is from 0 to 1, where the value of zero means that ranking lists are identical, and the value of one means that ranking lists are complete opposites.