Finally, the features with their corresponding targets are split into training, validation, and testing sets and stored for later usage in the model fine-tuning. The MTE offers two data partition schemes: random splitting where molecules are randomly assigned, and scaffold split where molecules are assigned to splits based on common structural motifs. Figure illustrates the feature transformation process of the SMILES data.