中文标题#
基于生化表示的多模态量子视觉变换器用于酶学委员会分类
英文标题#
Multimodal Quantum Vision Transformer for Enzyme Commission Classification from Biochemical Representations
中文摘要#
准确预测酶的功能仍然是计算生物学中的主要挑战，特别是对于结构注释或序列同源性有限的酶。 我们提出了一种新颖的多模态量子机器学习（QML）框架，通过整合四种互补的生化模态来增强酶委员会（EC）分类：蛋白质序列嵌入、量子衍生的电子描述符、分子图结构和 2D 分子图像表示。 量子视觉变换器（QVT）主干配备了特定于模态的编码器和统一的交叉注意力融合模块。 通过整合图特征和空间模式，我们的方法捕捉了酶功能背后的关键立体电子相互作用。 实验结果表明，我们的多模态 QVT 模型达到了 85.1% 的 top-1 准确率，显著优于仅使用序列的基线，并且与其他 QML 模型相比取得了更好的性能结果。
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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