G2T-LLM: Graph-to-Tree Text Encoding for Molecule Generation with Fine-Tuned Large Language Models

TL;DR

G2T-LLM introduces a hierarchical text encoding for molecules that leverages large language models to generate valid, diverse chemical structures with natural language interaction, showing competitive results on benchmarks.

Contribution

The paper presents a novel graph-to-tree text encoding method for molecule generation that enables LLMs to produce valid chemical structures with minimal task-specific tuning.

Findings

Achieved comparable performance to state-of-the-art methods on benchmarks.

Enabled natural language interaction for molecular design.

Generated diverse and valid molecular structures.

Abstract

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.