Table 2 first shows the performance of the D-MPNN models on the EXT-Zamora and EXT-SAMPL9 datasets. As described in Section "Dataset", the EXT-Zamora contains molecules that are similar to the training sets (LF-QC and HF-Exp) in terms of molecular weight and log P range, thereby providing insight into the predictive capability within a similar molecular space. In contrast, the EXT-SAMPL9 dataset consists of relatively larger molecules, allowing us to evaluate the models' generalization capabilities. We report the performance of various D-MPNN models, including single-task, transfer learning, multi-target learning, and feature-augmented learning.