This method assesses the relative impact of each input feature by comparing its contribution to the overall prediction, relative to an average baseline prediction. In this work, we utilized Chemprop v2's implementation of Shapley value analysis, which is based on the popular SHAP (SHapley Additive exPlanations) Python package and builds on the work by Li et al. , who performed Shapley analysis for additional quantum mechanical descriptors. This analysis enabled us to quantify the marginal contribution of individual atom and bond features to the final prediction. For further theoretical background on SHAP, we refer readers to the foundational work by Lundberg and Lee. We conducted the Shapley analysis using the primary dataset without any data augmentation. This analysis was performed for ML models trained on 80% of the dataset, using both the full native featurizer and the RIGR featurizer. To compute the Shapley values for each molecule in the test set, we used the PermutationExplainer from SHAP to calculate the expected Shapley value of each feature, sampling 1, 000 different combinations of included and excluded features during inference. This process was repeated for every molecule, and we then averaged the absolute Shapley values of each feature across all molecules to determine the expected average effect of each feature on the model's output. Since we have an ensemble of five models, we report averaged results to account for variations between different model initializations. Finally, we assessed the relative importance of the features by comparing the magnitudes of their corresponding Shapley values for the models trained using native and RIGR featurizers.