用树形文本编码让大模型更懂分子结构，生成更有效化学式。
G2T-LLM: Graph-to-Tree Text Encoding for Molecule Generation with Fine-Tuned Large Language Models
- 把分子图转成树状文本，适配大模型处理
- 微调后生成的分子结构合法且连贯
- 适合想用自然语言设计分子的研究者
我们提出G2T-LLM，一种新型分子生成方法，通过图到树的文本编码，将基于图的分子结构转化为层次化文本格式（如JSON、XML），以适配大语言模型（LLMs）的处理优势。该编码方式将复杂分子图转换为树状结构，使LLMs能更高效地理解与生成。通过监督微调，G2T-LLM可生成有效且连贯的化学结构，解决传统图方法中常见的无效输出问题。尽管大模型计算成本较高，但其具备更强泛化能力与适应性，仅需少量任务定制即可生成多样分子结构。在多个基准分子生成数据集上，其性能达到当前最优水平，展现出作为AI驱动分子设计灵活工具的巨大潜力。
原文摘要 · Abstract (English)
We introduce G2T-LLM, a novel approach for molecule generation that uses graph-to-tree text encoding to transform graph-based molecular structures into a hierarchical text format optimized for large language models (LLMs). This encoding converts complex molecular graphs into tree-structured formats, such as JSON and XML, which LLMs are particularly adept at processing due to their extensive pre-training on these types of data. By leveraging the flexibility of LLMs, our approach allows for intuitive interaction using natural language prompts, providing a more accessible interface for molecular design. Through supervised fine-tuning, G2T-LLM generates valid and coherent chemical structures, addressing common challenges like invalid outputs seen in traditional graph-based methods. While LLMs are computationally intensive, they offer superior generalization and adaptability, enabling the generation of diverse molecular structures with minimal task-specific customization. The proposed approach achieved comparable performances with state-of-the-art methods on various benchmark molecular generation datasets, demonstrating its potential as a flexible and innovative tool for AI-driven molecular design.
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