As a more quantitative approach, we devised a scoring function that summarizes the feature analysis and allows to estimate the confidence level of our predictions, see Section 2.6. The performance of our model is detailed in Supplemental Fig. , showcasing strong predictive capabilities with an F1-score (micro) of 0.92, an AUC of 0.94, and an AP of 0.81. Using a confidence threshold of 50%, leads to the expected increase in accuracy of the MCS-based predictions for both Jaworski's dataset and the Golden dataset, at a moderate decline in success rate, see Fig. . This observation underscores the robustness of the method in enhancing prediction reliability through the strategic application of a confidence threshold.