A consensus approach, averaging the predictions of the CatBoost and GCNN models, achieved the lowest RMSE values for data splitting by SMILES (0.70 for the full dataset and 0.67 lg(y 2 ) for the drug-like subset). This approach leverages the strengths of each model, contributing to a reduction in variance and systematic bias in the predictions. According to the literature, optimal results in consensus modeling are achieved by integrating heterogeneous methods or diverse molecular representations, further validating the effectiveness of the proposed approach.