{"ID":2873337,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06465","arxiv_id":"2509.06465","title":"CAME-AB: Cross-Modality Attention with Mixture-of-Experts for Antibody Binding Site Prediction","abstract":"Antibody binding site prediction plays a pivotal role in computational immunology and therapeutic antibody design. Existing sequence or structure methods rely on single-view features and fail to identify antibody-specific binding sites on the antigens. In this paper, we propose \\textbf{CAME-AB}, a novel Cross-modality Attention framework with a Mixture-of-Experts (MoE) backbone for robust antibody binding site prediction. CAME-AB integrates five biologically grounded modalities, including raw amino acid encodings, BLOSUM substitution profiles, pretrained language model embeddings, structure-aware features, and GCN-refined biochemical graphs, into a unified multimodal representation. To enhance adaptive cross-modal reasoning, we propose an \\emph{adaptive modality fusion} module that learns to dynamically weight each modality based on its global relevance and input-specific contribution. A Transformer encoder combined with an MoE module further promotes feature specialization and capacity expansion. We additionally incorporate a supervised contrastive learning objective to explicitly shape the latent space geometry, encouraging intra-class compactness and inter-class separability. To improve optimization stability and generalization, we apply stochastic weight averaging during training. Extensive experiments on benchmark antibody-antigen datasets demonstrate that CAME-AB consistently outperforms strong baselines on multiple metrics, including Precision, Recall, F1-score, AUC-ROC, and MCC. Ablation studies further validate the effectiveness of each architectural component and the benefit of multimodal feature integration. The model implementation details and the codes are available on https://anonymous.4open.science/r/CAME-AB-C525","short_abstract":"Antibody binding site prediction plays a pivotal role in computational immunology and therapeutic antibody design. Existing sequence or structure methods rely on single-view features and fail to identify antibody-specific binding sites on the antigens. In this paper, we propose \\textbf{CAME-AB}, a novel Cross-modality...","url_abs":"https://arxiv.org/abs/2509.06465","url_pdf":"https://arxiv.org/pdf/2509.06465v4","authors":"[\"Hongzong Li\",\"Jiahao Ma\",\"Zhanpeng Shi\",\"Rui Xiao\",\"Fanming Jin\",\"Ye-Fan Hu\",\"Hangjun Che\",\"Jian-Dong Huang\"]","published":"2025-09-08T09:24:09Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CE\",\"q-bio.BM\"]","methods":"[\"Transformer\",\"Language Model\"]","project_urls":"[\"https://anonymous.4open.science/r/CAME-AB-C525\"]","has_code":false}