LLaMo：基于大型语言模型的分子图辅助系统

LLaMo: Large Language Model-based Molecular Graph Assistant

October 31, 2024

作者: Jinyoung Park, Minseong Bae, Dohwan Ko, Hyunwoo J. Kim

cs.AI

摘要

大型语言模型（LLMs）通过指令调整展示出了出色的泛化能力和遵循指令的能力。LLMs和指令调整的进展导致了大型视觉语言模型（LVLMs）的发展。然而，LLMs和指令调整在分子领域的能力尚未得到充分探索。因此，我们提出了LLaMo：基于大型语言模型的分子图助手，这是一个端到端训练的大型分子图语言模型。为了弥合语言和图形模态之间的差异，我们提出了多级图投影器，通过抽象化每个GNN层的输出表示和基序表示，利用交叉注意机制将图表示转换为图标记。我们还引入了机器生成的分子图指令数据，用于指令调整大型分子图语言模型，以实现通用分子和语言理解。我们的广泛实验表明，LLaMo在各种任务上表现出最佳性能，如分子描述生成、性质预测和IUPAC命名预测。LLaMo的代码可在https://github.com/mlvlab/LLaMo 上找到。

English

Large Language Models (LLMs) have demonstrated remarkable generalization and instruction-following capabilities with instruction tuning. The advancements in LLMs and instruction tuning have led to the development of Large Vision-Language Models (LVLMs). However, the competency of the LLMs and instruction tuning have been less explored in the molecular domain. Thus, we propose LLaMo: Large Language Model-based Molecular graph assistant, which is an end-to-end trained large molecular graph-language model. To bridge the discrepancy between the language and graph modalities, we present the multi-level graph projector that transforms graph representations into graph tokens by abstracting the output representations of each GNN layer and motif representations with the cross-attention mechanism. We also introduce machine-generated molecular graph instruction data to instruction-tune the large molecular graph-language model for general-purpose molecule and language understanding. Our extensive experiments demonstrate that LLaMo shows the best performance on diverse tasks, such as molecular description generation, property prediction, and IUPAC name prediction. The code of LLaMo is available at https://github.com/mlvlab/LLaMo.