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

• URL: http://arxiv.org/abs/2410.02198v1
• Date: Thu, 3 Oct 2024 04:25:21 GMT
• Title: G2T-LLM: Graph-to-Tree Text Encoding for Molecule Generation with Fine-Tuned Large Language Models
• Authors: Zhaoning Yu, Xiangyang Xu, Hongyang Gao,
• Abstract summary: We introduce G2T-LLM, a novel approach that uses graph-to-tree text encoding to transform graph-based molecular structures into a hierarchical text format optimized for benchmark (LLMs) This encoding converts complex molecular graphs into tree-structured formats, such as large language models and XML, which LLMs are particularly adept at processing due to their extensive pre-training on these types of data. Through supervised fine-tuning, G2T-LLM generates valid and coherent chemical structures, addressing common challenges like invalid outputs seen in traditional graph-based methods.
• Score: 15.32011692129901
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• GraphXForm: Graph transformer for computer-aided molecular design with application to extraction [73.1842164721868]
  We present GraphXForm, a decoder-only graph transformer architecture, which is pretrained on existing compounds and then fine-tuned. We evaluate it on two solvent design tasks for liquid-liquid extraction, showing that it outperforms four state-of-the-art molecular design techniques.
  arXiv  Detail & Related papers  (2024-11-03T19:45:15Z)
• Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning [32.745100532916204]
  Large language models (LLMs) have integrated images, but adapting them to graphs remains challenging. We introduce Llamole, the first multimodal LLM capable of interleaved text and graph generation. Llamole significantly outperforms 14 adapted LLMs across 12 metrics for controllable molecular design and retrosynthetic planning.
  arXiv  Detail & Related papers  (2024-10-05T16:35:32Z)
• Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion Model [22.368332915420606]
  Unified Text-Graph Diffusion Model (UTGDiff) is a framework to generate molecular graphs from instructions. UTGDiff features a unified text-graph transformer as the denoising network, derived from pre-trained language models. Our experimental results demonstrate that UTGDiff consistently outperforms sequence-based baselines in tasks involving instruction-based molecule generation and editing.
  arXiv  Detail & Related papers  (2024-08-19T11:09:15Z)
• Crossing New Frontiers: Knowledge-Augmented Large Language Model Prompting for Zero-Shot Text-Based De Novo Molecule Design [0.0]
  Our study explores the use of knowledge-augmented prompting of large language models (LLMs) for the zero-shot text-conditional de novo molecular generation task. Our framework proves effective, outperforming state-of-the-art (SOTA) baseline models on benchmark datasets.
  arXiv  Detail & Related papers  (2024-08-18T11:37:19Z)
• Many-Shot In-Context Learning for Molecular Inverse Design [56.65345962071059]
  Large Language Models (LLMs) have demonstrated great performance in few-shot In-Context Learning (ICL) We develop a new semi-supervised learning method that overcomes the lack of experimental data available for many-shot ICL. As we show, the new method greatly improves upon existing ICL methods for molecular design while being accessible and easy to use for scientists.
  arXiv  Detail & Related papers  (2024-07-26T21:10:50Z)
• Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model [49.64512917330373]
  We introduce a multi-constraint molecular generation large language model, TSMMG, akin to a student. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers' We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements.
  arXiv  Detail & Related papers  (2024-03-20T02:15:55Z)
• GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text [25.979382232281786]
  We introduce GIT-Mol, a multi-modal large language model that integrates the Graph, Image, and Text information. We achieve a 5%-10% accuracy increase in properties prediction and a 20.2% boost in molecule generation validity.
  arXiv  Detail & Related papers  (2023-08-14T03:12:29Z)
• GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning [71.89623260998934]
  This study investigates the feasibility of employing natural language instructions to accomplish molecule-related tasks in a zero-shot setting. Existing molecule-text models perform poorly in this setting due to inadequate treatment of instructions and limited capacity for graphs. We propose GIMLET, which unifies language models for both graph and text data.
  arXiv  Detail & Related papers  (2023-05-28T18:27:59Z)
• Reinforced Molecular Optimization with Neighborhood-Controlled Grammars [63.84003497770347]
  We propose MNCE-RL, a graph convolutional policy network for molecular optimization. We extend the original neighborhood-controlled embedding grammars to make them applicable to molecular graph generation. We show that our approach achieves state-of-the-art performance in a diverse range of molecular optimization tasks.
  arXiv  Detail & Related papers  (2020-11-14T05:42:15Z)
• Self-Supervised Graph Transformer on Large-Scale Molecular Data [73.3448373618865]
  We propose a novel framework, GROVER, for molecular representation learning. GROVER can learn rich structural and semantic information of molecules from enormous unlabelled molecular data. We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.
  arXiv  Detail & Related papers  (2020-06-18T08:37:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.