SoCoBio: Socialized Coevolution Based Hierarchical Biomolecular Function Prediction

Biomolecular function prediction assigns sequences to functional taxonomies whose data-rich ancestors branch into a long tail of data-scarce leaf functions. Previous methods do not treat vertical generalization, which carries evidence from ancestors to tail classes, and horizontal discrimination, which distinguishes a class from its most similar siblings, as distinct interactions. To tackle this, we introduce SoCoBio, a hierarchical multi-label classifier for biomolecules that treats each class prototype as a learner in the sense of socialized learning. Whereas static hierarchical models weight relatives by a fixed rule, SoCoBio learns two interactions among prototypes, namely a Vertical Controller that passes gated information from each parent to its children and a Horizontal Controller that then lets each class select messages from a few similar siblings. A class thus receives shared context before it weighs messages from its closest siblings. Viewing SoCoBio as a within-predictor special case of socialized learning, we derive a sufficient condition for a learned update to lower the multi-label loss and a per-class direction bound whose required positive support grows only logarithmically with the number of classes. As a complete predictor on peptide and enzyme benchmarks and an independent enzyme test set, SoCoBio attains the highest leaf-level and mean-level F1 among the compared methods, and matched retraining with a short budget on the largest benchmark shows that its leaf-F1 gain from interaction, concentrated on tail classes, requires both controllers.