MoDANet: A Motif-Aware Distance-Masked Attention Network for Molecular Property Prediction

Reliable molecular property prediction is essential for drug discovery, yet standard graph Transformers use unrestricted global attention without explicitly organizing atom interactions by topological range. We introduce Motif-Aware Distance-Masked Attention Network (MoDANet), a dual-branch multi-scale graph Transformer whose global branch constructs fixed, nested, and permutation-equivariant neighborhoods from shortest-path distances and learns a layer-wise convex mixture over local, intermediate, and global attention scopes. Its motif branch directly encodes functional-group fragments obtained through BRICS decomposition. MoDANet provides an inspectable, distance-structured attention mechanism, is evaluated under scaffold splits on seven MoleculeNet benchmarks, and achieves state-of-the-art transfer performance after pretraining on a 250K-molecule ZINC subset.