用多视角分子表征提升语言模型对化学物质的理解能力
MV-CLAM: Multi-View Molecular Interpretation with Cross-Modal Projection via Language Model
- 通过多查询变换器将分子多视角表征对齐到统一文本空间
- 在分子检索与描述生成任务中显著提升准确率
- 适合需要精细分子理解的药物研发与化学智能场景
化学与生物医学领域的人类专业知识依赖于对分子的上下文理解，大语言模型可通过分子结构与文本间的细粒度对齐加以拓展。现有跨模态学习方法虽关注跨模态对齐，但忽略不同分子视图间的互补信息，依赖单视图表示，限制了分子理解深度。此外，简单的多视图对齐策略存在两个问题：(1) 各自独立的对齐空间导致分子与文本嵌入映射不一致；(2) 现有损失函数无法保留多样化分子特征以实现细粒度对齐。这限制了大语言模型对分子性质的全面理解。为此，我们提出MV-CLAM，一种新框架，利用多查询变换器（MQ-Former）将多视图分子表示对齐至统一文本空间。该方法确保跨视图一致性，并通过词元级对比损失保留分子特征多样性。实验表明，MV-CLAM增强了分子推理能力，在检索与描述生成任务中均取得更优表现。代码已开源：https://github.com/sumin124/mv-clam.git。
原文摘要 · Abstract (English)
Human expertise in chemistry and biomedicine relies on contextual molecular understanding, a capability that large language models (LLMs) can extend through fine-grained alignment between molecular structures and text. Recent multimodal learning advances focus on cross-modal alignment, but existing molecule-text models ignore complementary information in different molecular views and rely on single-view representations, limiting molecular understanding. Moreover, naïve multi-view alignment strategies face two challenges: (1) separate aligned spaces with inconsistent mappings between molecule and text embeddings, and that (2) existing loss objectives fail to preserve complementary information for fine-grained alignment. This can limit the LLM's ability to fully understand the molecular properties. To address these issues, we propose MV-CLAM, a novel framework that aligns multi-view molecular representations into a unified textual space using a multi-query transformer (MQ-Former). Our approach ensures cross-view consistency while a token-level contrastive loss preserves diverse molecular features across textual queries. MV-CLAM enhances molecular reasoning, improving retrieval and captioning accuracy. The source code of MV-CLAM is available in https://github.com/sumin124/mv-clam.git.
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