Despite high R 2 values ranging from 0.79 to 0.83, no definitive patterns emerged from the analysis that could distinctly highlight a successful set of parameters and descriptors. The R 2 values for models built with Random Forest were consistently higher than those using the XGBoost algorithm, with variations within a narrow range (up to 0.03). Moreover, the MAE values for XGBoost were greater than those for Random Forest, suggesting a superior performance of Random Forest in these evaluations. Similar to previous observations, embedding vectors for protein sequences were not among the top performers, indicating a lower information content compared to protein descriptors. Additionally, the presence of models using one-hot encoding in the top ten suggests that detailed amino acid descriptors contribute minimally to model quality.