Cardinality-Preserving Attention Channels for Graph Transformers in Molecular Property Prediction

Drug discovery motivates accurate molecular property prediction when labeled data are limited and candidate spaces are vast. This article presents CardinalGraphFormer, a graph transformer that augments structured attention with a query-conditioned gated unnormalized aggregation channel to preserve dynamic cardinality signals, complemented by graph-specific structural biases; a locality prior via sparse masking provides scalability for larger graphs. For typical drug-like molecules (K = 3 is near-global), masking acts mainly as a regularizer; for larger graphs it provides meaningful efficiency gains. Pretraining unifies contrastive alignment of augmented graph views and masked reconstruction of attributes. Evaluations on public benchmarks show consistent gains over baselines, isolated via controls for capacity, objectives, and size effects. Ablations confirm the cardinality channel’s contributions beyond simpler approximations, with efficiency benefits on large molecules. Code, artifacts, and protocols emphasize reproducibility.