Feature-Based Analysis of MS2 Data Reveals a Skeleton-Level Chemical Space and Enables Discovery of Sesquiterpenes...

Natural products bearing novel skeletons expand accessible chemical space and may inspire future biological discovery, yet their identification still relies largely on chance. This limitation stems largely from prevailing strategies for representing and interpreting MS2 data and not simply from spectral quality or algorithmic performance. Current workflows treat MS2 spectra as pairwise comparable entities, with structural relationships inferred from similarity, favoring analogues of known scaffolds while obscuring global structural organization. Here, we propose a feature-structured analytical paradigm that treats MS2 data as an integrated signal system shaped by molecular skeletons. By reorganizing spectra into fragment-feature representations and applying non-negative matrix factorization, structural information becomes directly observable at the data set level, revealing a skeleton-level chemical space (SLECS) in which compounds organize according to underlying skeletal features rather than pairwise similarity. Evaluation across diverse data sets shows that the SLECS-based framework resolves skeleton-level organization beyond conventional similarity-based approaches and enables systematic identification of structurally distinct regions. Application to Pilea cavaleriei led to the targeted isolation of seven sesquiterpenes (1-7), including compounds 1 and 2, featuring an unprecedented bicyclo[6.3.1] skeleton, thereby enabling targeted discovery of novel skeletons and expanding the pool of unexplored scaffolds for future biological evaluation. This work establishes a new representation paradigm for MS2 data analysis that complements similarity-based approaches and offers a scalable strategy for structure-oriented exploration of chemical space and the discovery of novel natural product scaffolds.