Notably, molecular descriptors are identified as particularly beneficial for most of the molecular property prediction tasks we examine. However, variations in the utility of descriptor types are also observed across different datasets. For example, atom and bond descriptors seem to benefit ESOL and FreeSolv model performance to varying degrees (Fig- ). To further quantify and elucidate the impact of different descriptors on model predictions, we calculated Shapley values for each input feature in the ESOL and FreeSolv datasets (Figure and S7), following the procedures outlined in the Methods section. The Shapley value analysis yields several important insights. First, atom QM descriptors emerge as the most influential predictors for both datasets, agreeing with the results from models trained on subsets of descriptors (Figure ). This finding also strongly suggests a critical role for atom-level QM properties in capturing molecular characteristics related to solvation.