G2T-LLM: Graph-to-Tree Text Encoding for Molecule Generation with Fine-Tuned Large Language Models
<|reference_start|>G2T-LLM: Graph-to-Tree Text Encoding for Molecule Generation with Fine-Tuned Large Language Models: 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.<|reference_end|>
@article{yu2024g2t-llm:,
    title={G2T-LLM: Graph-to-Tree Text Encoding for Molecule Generation with
  Fine-Tuned Large Language Models},
    author={Zhaoning Yu, Xiangyang Xu, Hongyang Gao},
    journal={arXiv preprint arXiv:2410.02198},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02198},
    primaryClass={cs.LG cs.AI q-bio.QM}
}