融合序列与结构信息，提升肽类预测模型性能
M2oE: Multimodal Collaborative Expert Peptide Model
- 采用专家网络与交叉注意力机制融合序列和结构信息
- 在低信息模态数据上表现优于单模态模型
- 适合药物设计中复杂肽功能预测任务
肽是由氨基酸组成的生物分子，在人体中发挥重要作用。近年来，肽在药物设计与合成中受到广泛关注，肽预测任务有助于发现功能性肽。通常，模型基于肽的初级序列和结构信息进行编码。然而，现有研究多依赖单一模态信息（结构或序列），缺乏多模态方法。我们发现，单模态模型在某一模态信息不足的数据集上表现较差。为此，本文提出M2oE多模态协同专家肽模型。基于前期工作，通过整合序列与空间结构信息，引入专家模型与交叉注意力机制，有效平衡并提升了模型能力。实验结果表明，M2oE在复杂任务预测中表现优异。
原文摘要 · Abstract (English)
Peptides are biomolecules comprised of amino acids that play an important role in our body. In recent years, peptides have received extensive attention in drug design and synthesis, and peptide prediction tasks help us better search for functional peptides. Typically, we use the primary sequence and structural information of peptides for model encoding. However, recent studies have focused more on single-modal information (structure or sequence) for prediction without multi-modal approaches. We found that single-modal models are not good at handling datasets with less information in that particular modality. Therefore, this paper proposes the M2oE multi-modal collaborative expert peptide model. Based on previous work, by integrating sequence and spatial structural information, employing expert model and Cross-Attention Mechanism, the model's capabilities are balanced and improved. Experimental results indicate that the M2oE model performs excellently in complex task predictions.
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