用子结构+描述符提升分子表示，模型预测更准。
Thinking like a CHEMIST: Combined Heterogeneous Embedding Model Integrating Structure and Tokens
- 将分子拆成子结构，用描述符生成化学相关特征
- 在QSAR预测中优于传统方法，提升显著
- 融合语言模型与图神经网络，适合化学建模者
有效表示分子结构仍是化学领域中的挑战。语言模型和图模型在多项任务中表现优异，但多数数据集和语言模型采用SMILES格式，存在明显局限。本文提出一种新方法：将分子分解为子结构，计算其描述符表示，提供更精细且化学相关的输入。该子结构与描述符数据同时输入语言模型，并设计双模态架构，融合语言模型（如RoBERTa）与图模型（如GIN、GCN、Graphormer）。实验表明，该框架在定量结构-活性关系（QSAR）等任务中显著优于传统方法。
原文摘要 · Abstract (English)
Representing molecular structures effectively in chemistry remains a challenging task. Language models and graph-based models are extensively utilized within this domain, consistently achieving state-of-the-art results across an array of tasks. However, the prevailing practice of representing chemical compounds in the SMILES format - used by most data sets and many language models - presents notable limitations as a training data format. In this study, we present a novel approach that decomposes molecules into substructures and computes descriptor-based representations for these fragments, providing more detailed and chemically relevant input for model training. We use this substructure and descriptor data as input for language model and also propose a bimodal architecture that integrates this language model with graph-based models. As LM we use RoBERTa, Graph Isomorphism Networks (GIN), Graph Convolutional Networks (GCN) and Graphormer as graph ones. Our framework shows notable improvements over traditional methods in various tasks such as Quantitative Structure-Activity Relationship (QSAR) prediction.
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