{"ID":2880339,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14844","arxiv_id":"2508.14844","title":"Multimodal Quantum Vision Transformer for Enzyme Commission Classification from Biochemical Representations","abstract":"Accurately predicting enzyme functionality remains one of the major challenges in computational biology, particularly for enzymes with limited structural annotations or sequence homology. We present a novel multimodal Quantum Machine Learning (QML) framework that enhances Enzyme Commission (EC) classification by integrating four complementary biochemical modalities: protein sequence embeddings, quantum-derived electronic descriptors, molecular graph structures, and 2D molecular image representations. Quantum Vision Transformer (QVT) backbone equipped with modality-specific encoders and a unified cross-attention fusion module. By integrating graph features and spatial patterns, our method captures key stereoelectronic interactions behind enzyme function. Experimental results demonstrate that our multimodal QVT model achieves a top-1 accuracy of 85.1%, outperforming sequence-only baselines by a substantial margin and achieving better performance results compared to other QML models.","short_abstract":"Accurately predicting enzyme functionality remains one of the major challenges in computational biology, particularly for enzymes with limited structural annotations or sequence homology. We present a novel multimodal Quantum Machine Learning (QML) framework that enhances Enzyme Commission (EC) classification by integr...","url_abs":"https://arxiv.org/abs/2508.14844","url_pdf":"https://arxiv.org/pdf/2508.14844v1","authors":"[\"Murat Isik\",\"Mandeep Kaur Saggi\",\"Humaira Gowher\",\"Sabre Kais\"]","published":"2025-08-20T16:56:41Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Vision Transformer\",\"Transformer\"]","has_code":false}