Molecules in the datasets were filtered using the JTVAE vocabulary set to ensure validity within the defined chemical space of the VAE model. We partitioned the data using a stratified scaffold splitting strategy, where molecules between training and external validation perceived non-overlapping Bemis-Murcko scaffolds while retaining the distributions of the target values in the two datasets. With this strategy, splitting the data into an exact partition could be impossible. Therefore, we optimized the proportion of molecules in the test set to a range of 10-20%, which corresponds to ratios of training and test sets between 90:10 and 80:20. In the data engineering stage, features with low variance (less than 0.05) or those missing in over 50% of data were excluded; otherwise, they were imputed using k-nearest neighbors (KNN) imputation with k = 5.