Additionally, since each protein was modeled using three different optimized classifications (one per selected metric balanced accuracy, precision and F1), a fourth integrative model was developed by using a soft-voting method 51 implemented by calculating the average of the classification probabilities provided by each of the three independent models' results. In this ensemble, each of the three models contributes a vote weighted by its confidence in the classification, calculated from the probability of belonging to the assigned class. For example, if two models predict a molecule as 'active' with high certainty probabilities, and the third classifies it as 'inactive' with a lower probability, the soft-voting XGBoost ensemble computes an arithmetic average of these weighted probabilities to assign the final classification to 'active'. This method capitalizes on the specific information on each model and mitigates the risk of isolated errors from a single model, thus enhancing the robustness and accuracy of the final classification. By employing this soft voting approach, the ensemble model ensures that the final verdict is based on an informed and balanced consensus, significantly improving the reliability of predictions in practical applications.