To ensure the practical applicability of our models, we simulate scenarios where the drug structures of interest evolve over time. This is achieved through the adoption of a scaffold split strategy, wherein the data is partitioned into train, validation, and test sets, followed by the settings in the TDC leaderboard. Our choice of evaluation metrics also aligns with the TDC leaderboard settings. For classification tasks, we utilize the area under the receiver-operating characteristic curve (AUROC) and the area under the precision-recall curve (PRAUC), both ranging from 0 to 1, with higher values indicating better performance.