INB3P: A Multi-Modal and Interpretable Co-Attention Framework Integrating Property-Aware Explanations and Memory-Bank Contrastive Fusion for Blood-Brain Barrier Penetrating Peptide Discovery.

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, INB3P 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. INB3P integrates PCGM with a bi-directional co-attention mechanism fusing sequence and structure, optimized via contrastive learning and a Stable-MCC loss. INB3P 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.