中文标题#
基於生化表示的多模態量子視覺變換器用於酶學委員會分類
英文标题#
Multimodal Quantum Vision Transformer for Enzyme Commission Classification from Biochemical Representations
中文摘要#
準確預測酶的功能仍然是計算生物學中的主要挑戰，特別是對於結構註釋或序列同源性有限的酶。我們提出了一種新穎的多模態量子機器學習（QML）框架，通過整合四種互補的生化模態來增強酶委員會（EC）分類：蛋白質序列嵌入、量子衍生的電子描述符、分子圖結構和 2D 分子圖像表示。量子視覺變換器（QVT）主幹配備了特定於模態的編碼器和統一的交叉注意力融合模塊。通過整合圖特徵和空間模式，我們的方法捕捉了酶功能背後的關鍵立體電子相互作用。實驗結果表明，我們的多模態 QVT 模型達到了 85.1% 的 top-1 準確率，顯著優於僅使用序列的基線，並且與其他 QML 模型相比取得了更好的性能結果。
英文摘要#
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.
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