标题： 双模态表征学习用于分子性质预测 显示英文标题

标题： Dual-Modality Representation Learning for Molecular Property Prediction

摘要： 分子属性预测近年来引起了广泛关注。 准确预测药物属性很大程度上依赖于有效的分子表示。 化学化合物的结构通常以图或SMILES序列的形式表示。 在学习药物属性方面的最新进展通常采用基于图表示的图神经网络（GNNs）。 对于SMILES表示，Transformer-based架构已被采用，将每个SMILES字符串视为标记序列。 由于每种表示方法都有其优缺点，结合两种表示方法在学习药物属性方面是一个有前景的方向。 我们提出了一种名为双模态交叉注意力（DMCA）的方法，该方法通过采用交叉注意力机制，可以有效结合两种表示的优势。 DMCA在八个数据集上进行了评估，包括分类和回归任务。 结果表明，我们的方法整体表现最佳，突显了其在利用图和SMILES模态互补信息方面的有效性。 显示英文摘要

摘要： Molecular property prediction has attracted substantial attention recently. Accurate prediction of drug properties relies heavily on effective molecular representations. The structures of chemical compounds are commonly represented as graphs or SMILES sequences. Recent advances in learning drug properties commonly employ Graph Neural Networks (GNNs) based on the graph representation. For the SMILES representation, Transformer-based architectures have been adopted by treating each SMILES string as a sequence of tokens. Because each representation has its own advantages and disadvantages, combining both representations in learning drug properties is a promising direction. We propose a method named Dual-Modality Cross-Attention (DMCA) that can effectively combine the strengths of two representations by employing the cross-attention mechanism. DMCA was evaluated across eight datasets including both classification and regression tasks. Results show that our method achieves the best overall performance, highlighting its effectiveness in leveraging the complementary information from both graph and SMILES modalities.