Decoupling topological and molecular features for interpretable biomolecular interaction prediction.

Predicting biomolecular interactions is fundamental to understanding cellular mechanisms and advancing drug discovery. However, biomolecular interactions exhibit immense diversity across multiple dimensions. Most existing computational methods are designed to handle one specific task or data modality, which limits their applicability and generalization capability in broader scenarios. To address this methodological rigidity, we propose a flexible framework for multi-modal feature fusion in biomolecular interaction prediction (FlexBIP). The core of FlexBIP lies in its modular architecture, which decouples intrinsic molecular features from complex graph topologies, enabling the adaptive integration of node attributes, edge properties, and auxiliary graph information. The flexible fusion methodology breaks through the limitations of task-specific models. This design enables FlexBIP to adaptively process and integrate biological data of different types and from various sources, including homogeneous interactions between molecules of the same type, heterogeneous interactions between different molecular classes, as well as qualitative binary, multi-class, and quantitative regression prediction tasks. Our research has yielded exciting results. In extensive testing across 15 benchmark datasets, covering 8 major categories of biomolecular associations, FlexBIP's performance comprehensively surpasses that of 25 state-of-the-art specialized models. Crucially, in data-scarce "cold-start" scenarios that simulate the discovery of new molecules, FlexBIP continues to demonstrate remarkable robustness and predictive accuracy. Furthermore, FlexBIP provides robust and reliable interpretability for various downstream analysis tasks.