  Self-supervised learning has recently gained growing interest in molecular
modeling for scientific tasks such as AI-assisted drug discovery. Current
studies consider leveraging both 2D and 3D molecular structures for
representation learning. However, relying on straightforward alignment
strategies that treat each modality separately, these methods fail to exploit
the intrinsic correlation between 2D and 3D representations that reflect the
underlying structural characteristics of molecules, and only perform
coarse-grained molecule-level alignment. To derive fine-grained alignment and
promote structural molecule understanding, we introduce an atomic-relation
level "blend-then-predict" self-supervised learning approach, MoleBLEND, which
first blends atom relations represented by different modalities into one
unified relation matrix for joint encoding, then recovers modality-specific
information for 2D and 3D structures individually. By treating atom
relationships as anchors, MoleBLEND organically aligns and integrates visually
dissimilar 2D and 3D modalities of the same molecule at fine-grained atomic
level, painting a more comprehensive depiction of each molecule. Extensive
experiments show that MoleBLEND achieves state-of-the-art performance across
major 2D/3D molecular benchmarks. We further provide theoretical insights from
the perspective of mutual-information maximization, demonstrating that our
method unifies contrastive, generative (cross-modality prediction) and
mask-then-predict (single-modality prediction) objectives into one single
cohesive framework.
