INB ³ P: A Multi‐Modal and Interpretable Co‐Attention Framework Integrating Property‐Aware Explanations and Memory‐Bank Contrastive Fusion for Blood–Brain Barrier Penetrating Peptide Discovery

ABSTRACT Functional peptide discovery, particularly for blood–brain barrier‐penetrating peptides (BBBPPs), is strictly limited by extreme data scarcity and the “black‐box” nature of deep learning. Here, INB ³ P is presented as a physics‐informed, multi‐modal framework designed to address these challenges. Physicochemical‐guided mutagenesis (PCGM), a novel augmentation strategy that enforces biochemical constraints to expand training diversity without violating the biological manifold. INB ³ P integrates PCGM with a bi‐directional co‐attention mechanism fusing sequence and structure, optimized via contrastive learning and a Stable‐MCC loss. INB ³ P significantly outperforms state‐of‐the‐art baselines on the same independent test set used in a prior study. Crucially, the model autonomously rediscovers known biophysical mechanisms—including amphipathic motifs and long‐range contact stabilization—providing strong in silico validation of its learned representations. This work establishes a generalizable paradigm for learning from small, imbalanced biological datasets. To facilitate community adoption, a web server is provided at http://www.bioai‐lab.com/INBP , featuring a standalone PCGM module, empowering researchers to apply physics‐guided augmentation strategy to their own sparse datasets.