Multimodal Quantum Vision Transformer for Enzyme Commission Classification from Biochemical Representations

--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. 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. Enzymes play a pivotal role in virtually every aspect of biological chemistry, acting as highly specialized catalysts that drive metabolic pathways, DNA replication, cell signaling, and other essential processes in living organisms [1], [2]. Consequently, the ability to accurately predict enzyme function has immense implications, facilitating the discovery of novel biocatalysts, guiding metabolic engineering efforts, and accelerating drug development.