用交叉注意力融合图与SMILES表示，提升分子性质预测准确率
Dual-Modality Representation Learning for Molecular Property Prediction
- 通过交叉注意力机制融合分子图与SMILES序列信息
- 在8个数据集上表现最优，分类与回归任务均领先
- 适合需要多模态分子表征的研究者参考
分子性质预测近年来受到广泛关注。药物性质的准确预测高度依赖于有效的分子表示。化学化合物的结构通常以图或SMILES序列形式表示。当前学习药物性质的方法普遍采用基于图表示的图神经网络（GNN）。对于SMILES表示，已有研究将其视为词元序列，采用基于Transformer的架构。由于两种表示各有优劣，结合两者进行学习是富有前景的方向。本文提出双模态交叉注意力（DMCA）方法，利用交叉注意力机制有效整合图与SMILES表示的优势。DMCA在包含分类与回归任务的8个数据集上进行了评估，结果表明该方法整体性能最佳，凸显了其在利用两种模态互补信息方面的有效性。
原文摘要 · Abstract (English)
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.
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