PAC-Net: A physics-guided multimodal hybrid network for antibody-antigen interaction prediction

The precise prediction of Antibody-Antigen Interaction (AAI) is a pivotal task for accelerating antibody drug discovery and virtual screening. To address the challenges of suboptimal multimodal fusion and the paucity of physical interpretability in existing approaches, this paper proposes PAC-Net, a physical prior-guided end-to-end deep learning framework. First, the model incorporates a Gated Multimodal Fusion mechanism that effectively integrates sequence semantics with structural features via dynamic weight allocation, thereby achieving adaptive alignment of multimodal information. Furthermore, the core Physics-Guided Hybrid Interaction Module encodes biophysical laws, including charge complementarity and hydrophobic interactions, directly as inductive biases for the attention mechanism. By integrating these biases with parallel depthwise separable convolutions within a unified architecture, the model synergistically captures both global long-range dependencies and local structural patterns among residues. Experimental results on two public datasets, HIV and CoV-AbDab, demonstrate that PAC-Net significantly outperforms current state-of-the-art methods in terms of prediction accuracy and robustness. Particularly in highly challenging antibody and antigen cold-start scenarios, the model exhibits exceptional cross-entity generalization performance, driven by its two innovative mechanisms: gated multimodal fusion and physical rule-guided attention. Consequently, PAC-Net provides a high-precision and interpretable computational tool for the virtual screening of antibody therapeutics. The source codes are publicly available at the following link https://github.com/WeiSongJian/PAC-Net.