标题： ProtoMol：通过原型引导的多模态学习增强分子属性预测 显示英文标题

标题： ProtoMol: Enhancing Molecular Property Prediction via Prototype-Guided Multimodal Learning

摘要： 多模态分子表示学习，联合建模分子图及其文本描述，通过整合结构和语义信息，增强了药物毒性、生物活性和理化性质预测的准确性与可解释性。 然而，现有的多模态方法存在两个关键限制：(1) 它们通常仅在最终编码器层进行跨模态交互，从而忽略了层次化的语义依赖关系；(2) 缺乏一个统一的原型空间以实现模态之间的鲁棒对齐。 为了解决这些限制，我们提出了 ProtoMol，一种基于原型引导的多模态框架，能够实现分子图和文本描述之间的细粒度集成和一致的语义对齐。 ProtoMol结合了双分支分层编码器，利用图神经网络处理结构化的分子图，并利用 Transformer 对非结构化文本进行编码，从而生成全面的逐层表示。 然后，ProtoMol引入了一种逐层双向跨模态注意力机制，逐步对齐各层之间的语义特征。 此外，构建了一个具有可学习的、类特定锚点的共享原型空间，以引导两种模态走向连贯且具有区分性的表示。 在多个基准数据集上的广泛实验表明，ProtoMol在多种分子属性预测任务中始终优于最先进的基线方法。 显示英文摘要

摘要： Multimodal molecular representation learning, which jointly models molecular graphs and their textual descriptions, enhances predictive accuracy and interpretability by enabling more robust and reliable predictions of drug toxicity, bioactivity, and physicochemical properties through the integration of structural and semantic information. However, existing multimodal methods suffer from two key limitations: (1) they typically perform cross-modal interaction only at the final encoder layer, thus overlooking hierarchical semantic dependencies; (2) they lack a unified prototype space for robust alignment between modalities. To address these limitations, we propose ProtoMol, a prototype-guided multimodal framework that enables fine-grained integration and consistent semantic alignment between molecular graphs and textual descriptions. ProtoMol incorporates dual-branch hierarchical encoders, utilizing Graph Neural Networks to process structured molecular graphs and Transformers to encode unstructured texts, resulting in comprehensive layer-wise representations. Then, ProtoMol introduces a layer-wise bidirectional cross-modal attention mechanism that progressively aligns semantic features across layers. Furthermore, a shared prototype space with learnable, class-specific anchors is constructed to guide both modalities toward coherent and discriminative representations. Extensive experiments on multiple benchmark datasets demonstrate that ProtoMol consistently outperforms state-of-the-art baselines across a variety of molecular property prediction tasks.