eBOND: An Electron-Guided Fragmentation Dataset for Molecular Learning

Molecular fragmentation, which decomposes molecules into smaller substructures, is widely used in fragment-based molecular learning. Existing fragmentation schemes primarily determine cleavage sites using hand-crafted chemical rules or cleavage patterns derived from reaction data. These approaches produce fragment spaces shaped by pre-defined structural assumptions or the coverage of recorded reactions, while largely overlooking molecule-specific electronic structure. Electronic structure underlies molecular properties, motivating its use in molecular fragmentation. However, obtaining such electronic information at scale requires costly quantum-chemical calculations, limiting the development of large-scale electron-guided fragmentation datasets. In this work, we introduce eBOND, a large-scale quantum-chemical dataset for electron-guided molecular fragmentation, covering 452,775 molecules at a computational cost of approximately 1.26 million CPU core-hours. Using three-state density functional theory and wave-function analysis, eBOND provides atom- and bond-level electronic descriptors and translates them into molecule-specific scores for fragmentation. On USPTO-50K, eBOND-derived cuts cover 81.1% of recorded synthetic disconnections and recover 73.4% of those missed by both BRICS and RECAP. We further show that eBOND-derived fragments provide useful learning signals for retrosynthesis and fragment-based molecular optimization. These results demonstrate the value of molecule-specific electronic information for molecular fragmentation and establish eBOND as a reusable resource for fragment-based molecular learning.