This approach allows for the determination of feature importance and enables model inter- For our Shapley value analysis, we focused on the ESOL and FreeSolv datasets, as we have calculated true QM descriptors for these datasets. The analysis was conducted independently on ML models trained on 80% of each dataset. For each ML model, we computed Shapley values for all features across all molecules in the corresponding dataset. Specifically, for each molecule, we calculated the expected Shapley value of each feature using the Per-mutationExplainer, sampling 1,000 different combinations of included and excluded features during prediction time. This process was repeated for all molecules in both datasets. 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 outcome. As an implementation note, we computed Shapley values using scaled feature and target values to ensure fair comparison across different property scales. The scaling procedure follows Chemprop's default implementation, resulting in scaled data with a mean of zero and standard deviation of one.