Large language models have made significant strides in natural language processing, enabling innovative applications in molecular science by processing textual representations of molecules. However, most existing language models cannot capture the rich information with complex molecular structures or images. In this paper, we introduce GIT-Mol, a multi-modal large language model that integrates the Graph, Image, and Text information. To facilitate the integration of multi-modal molecular data, we propose GIT-Former, a novel architecture that is capable of aligning all modalities into a unified latent space. We achieve a 5%-10% accuracy increase in properties prediction and a 20.2% boost in molecule generation validity compared to the baselines. With the any-to-language molecular translation strategy, our model has the potential to perform more downstream tasks, such as compound name recognition and chemical reaction prediction.

翻译：大语言模型在自然语言处理领域取得了显著进展，通过处理分子的文本表示，为分子科学带来了创新应用。然而，现有的大语言模型大多无法捕捉复杂分子结构或图像中的丰富信息。本文提出了GIT-Mol，一种融合图、图像与文本信息的多模态大语言模型。为整合多模态分子数据，我们设计了GIT-Former，这是一种能够将所有模态对齐到统一潜在空间的新型架构。与基线模型相比，我们在性质预测方面实现了5%-10%的精度提升，在分子生成有效性方面提升了20.2%。借助任意模态到语言的分子翻译策略，我们的模型有望执行更多下游任务，如化合物名称识别和化学反应预测。