MolFiber: From Global Anchors to Local Refinement in Molecular Learning

Molecular property prediction increasingly relies on combining complementary representations, yet standard fusion can be ineffective when one representation is already strong. In this regime, the key question is not how to combine all views globally, but where additional representations provide information that the strong representation misses. We introduce **MolFiber**, a Morgan-anchored framework that treats Morgan fingerprints as a global reference geometry for chemical space and uses GraphGrid and MolFormer as local refiners within neighborhoods of Morgan-similar molecules. Rather than acting as competing global predictors, the auxiliary views resolve distinctions that Morgan cannot see locally. **MolFiber-G** uses the full fiber-conditioned auxiliary evidence, whereas **MolFiber-L** uses only locally centered contrast; their ensemble, **MolFiber-ENS**, combines these complementary inductive biases. Across 24 molecular property prediction benchmarks, MolFiber-ENS achieves the strongest aggregate performance among the compared methods and outperforms controlled global-fusion baselines using the same representations. These results support a broader principle: complementary representations can be most valuable not when fused globally, but when used to refine the local ambiguities of a strong anchor.