Multimodal Quantum Vision Transformer for Enzyme Commission Classification from Biochemical Representations

2025-08-20

AI Summary

Enzyme function prediction—particularly for low-homology or structurally unannotated enzymes—remains a fundamental challenge in computational biology. To address this, we propose QVT, the first multimodal quantum machine learning framework integrating four complementary biochemical modalities: protein sequences, quantum-derived electronic descriptors, molecular graph structures, and 2D molecular images. Methodologically, QVT employs modality-specific encoders coupled with a cross-modal attention fusion mechanism, enabling joint representation learning of multiscale biochemical and quantum features within a unified architecture. Key technical innovations include quantum descriptor extraction, graph neural network–based molecular encoding, convolutional image feature learning, and multi-head cross-modal attention integration. On the EC number classification task, QVT achieves 85.1% top-1 accuracy, substantially outperforming unimodal baselines and state-of-the-art quantum machine learning models. This work establishes a novel, interpretable, and high-accuracy paradigm for enzyme functional annotation.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Accurately predicting enzyme functionality from limited structural annotations
• Enhancing Enzyme Commission classification using multimodal biochemical representations
• Integrating quantum-derived descriptors with molecular structures for enzyme function

Innovation

• Multimodal Quantum Machine Learning framework
• Quantum Vision Transformer with cross-attention fusion
• Integrates sequence, electronic, graph, image representations