Molecular property prediction is crucial for drug discovery when labeled data are scarce. This work presents \modelname, a graph transformer augmented with a query-conditioned cardinality-preserving attention (CPA) channel that retains dynamic support-size signals complementary to static centrality embeddings. The approach combines structured sparse attention with Graphormer-inspired biases (shortest-path distance, centrality, direct-bond features) and unified dual-objective self-supervised pretraining (masked reconstruction and contrastive alignment of augmented views). Evaluation on 11 public benchmarks spanning MoleculeNet, OGB, and TDC ADMET demonstrates consistent improvements over protocol-matched baselines under matched pretraining, optimization, and hyperparameter tuning. Rigorous ablations confirm CPA's contributions and rule out simple size shortcuts. Code and reproducibility artifacts are provided.

翻译：分子性质预测在标记数据稀缺时对药物发现至关重要。本文提出一种图Transformer模型，该模型通过查询条件化的基数保持注意力通道进行增强，该通道保留了与静态中心性嵌入互补的动态支持规模信号。该方法将结构化稀疏注意力与受Graphormer启发的偏置相结合，并采用统一的双目标自监督预训练策略。在涵盖MoleculeNet、OGB和TDC ADMET的11个公开基准测试上的评估表明，在匹配的预训练、优化和超参数调优条件下，该方法相较于协议匹配的基线模型实现了持续的性能提升。严格的消融实验证实了基数保持注意力通道的有效贡献，并排除了简单的规模捷径效应。代码与可复现性材料已开源提供。