Training the model on the drug-like subset using the SMILES protocol reduced RMSE for CatBoost:CDK from 0.74 to 0.69 lg(y 2 ) compared to the full dataset, and for GCNN:RDKit, from 0.77 to 0.71 lg(y 2 ). Using the scaffold protocol, the RMSE for CatBoost:CDK decreased from 0.85 to 0.82 lg(y 2 ), and for GCNN:RDKit, from 0.91 to 0.83 lg(y 2 ). Separation of drug-like molecules increased the homogeneity of the sample, improving prediction accuracy within this subgroup. The GCNN:RDKit method demonstrated a particularly noticeable improvement, approaching the performance of CatBoost:CDK. This higher performance may be due to the graph-based model's ability to better handle organic compounds with similar topology and atomic types when trained predominantly on such structures. At the same time, it may struggle to account for unique atoms and interactions characteristic of salts and large molecules present in the full dataset. However, it should be noted that such filtering limits the model's applicability to a broader range of substances. Table presents the final results for models based on the full and drug-like datasets, validated using three different protocols.