In order to maximize the training dataset, we collected data from four different sources, which are further referenced as datasets A (Boobier et al. ), B (Panapitiya ), C (Cui, Q. et ), and D ) which have already been used in the literature. An additional dataset E (Huuskonen ) was reserved as a dataset for testing the model. Merged the four datasets, resulting in a total of 28,859 samples for training and 1,291 samples for testing. Table and Figure give additional information about the process of data collection and data curation for the data that was used as a training dataset to train the model and the test dataset on which the model has been evaluated. To ensure the consistency of the combined dataset A-D, in particular to prevent having included duplicates, the representation of chemical structures is unified by converting the molecular representations into the canonical SMILES format. This standardization step eliminates inconsistencies caused by disparate chemical notations or representations. In parallel, the information on the solubility was harmonized by converting the solubility values of all data, e.g. from g/L into logS. The obtained datasets with harmonized logS properties were searched for equal SMILES codes, indicating duplicates in the dataset. Molecules with two or more records were sorted into two groups: molecules appearing multiple times having the same solubility values, and those where the solubility values differ. In the first case, we kept only one of the equal examples.