MolFM：一种多模态分子基础模型
MolFM: A Multimodal Molecular Foundation Model
AI总结：
针对现有多模态分子基础模型未利用知识图谱、跨模态关联捕捉不足的问题，提出融合分子结构、文本与知识图谱的MolFM模型，通过跨模态注意力实现联合表示学习，在跨模态检索等任务上取得最优性能。
摘要：
分子知识存在于三种不同模态的信息源中：分子结构、生物医学文献和知识库。有效整合来自这些模态的分子知识对推动生物医学研究至关重要。然而，现有的多模态分子基础模型在捕捉分子结构与文本之间的复杂关联方面存在局限，更重要的是，它们均未尝试利用源自知识图谱的丰富分子专业知识。本研究提出MolFM——一种多模态分子基础模型，旨在实现从分子结构、生物医学文本和知识图谱中进行联合表示学习。我们提出在分子结构的原子、分子实体的邻居以及语义相关文本之间建立跨模态注意力机制，以促进跨模态理解。理论分析表明，我们的跨模态预训练通过最小化同一分子不同模态之间、以及具有相似结构或功能的分子之间在特征空间中的距离，能够捕捉局部和全局分子知识。MolFM在多种下游任务上取得了当前最优性能。在跨模态检索任务中，MolFM在零样本和微调设置下分别以12.13%和5.04%的绝对提升优于现有模型。此外，定性分析展示了MolFM具备从分子子结构和知识图谱中提供依据的隐式能力。代码和模型可在https://github.com/BioFM/OpenBioMed获取。
英文摘要：
Molecular knowledge resides within three different modalities of information sources: molecular structures, biomedical documents, and knowledge bases. Effective incorporation of molecular knowledge from these modalities holds paramount significance in facilitating biomedical research. However, existing multimodal molecular foundation models exhibit limitations in capturing intricate connections between molecular structures and texts, and more importantly, none of them attempt to leverage a wealth of molecular expertise derived from knowledge graphs. In this study, we introduce MolFM, a multimodal molecular foundation model designed to facilitate joint representation learning from molecular structures, biomedical texts, and knowledge graphs. We propose cross-modal attention between atoms of molecular structures, neighbors of molecule entities and semantically related texts to facilitate cross-modal comprehension. We provide theoretical analysis that our cross-modal pre-training captures local and global molecular knowledge by minimizing the distance in the feature space between different modalities of the same molecule, as well as molecules sharing similar structures or functions. MolFM achieves state-of-the-art performance on various downstream tasks. On cross-modal retrieval, MolFM outperforms existing models with 12.13% and 5.04% absolute gains under the zero-shot and fine-tuning settings, respectively. Furthermore, qualitative analysis showcases MolFM's implicit ability to provide grounding from molecular substructures and knowledge graphs. Code and models are available on https://github.com/BioFM/OpenBioMed.
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