Various methods can be employed to explain model performance due to particular features and potentially be implemented with Chemprop. In this study, we demonstrated the use of Shapley value analysis as a valuable tool for identifying meaningful descriptors and building more explainable models. Results from the Shapley value analysis not only corroborates the importance of including QM descriptors but also provides a quantitative measure of their impact, even in scenarios where their benefits might not be immediately apparent from overall model performance metrics. Furthermore, the analysis offers valuable insights into the