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This research aims to classify and summarize models that integrate Graph Neural Networks and Large Language Models for molecular science applications.

June 17, 2026BioinformaticsOpen Access

A survey of models composed of Graph Neural Networks and Large Language Models for Molecular Science

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This research aims to classify and summarize models that integrate Graph Neural Networks and Large Language Models for molecular science applications.
Classified recent models that combine GNNs and LLMs based on their architecture, input-output design, and application scopes.
Presented tables detailing properties and parameters of the models along with their chemical computational applications.
Conducted a quantitative comparison of models based on their reported experimental results.
Developed a new classification system for GNN-LLM models to assist researchers in understanding their applications.
Summarized multiple models and their properties, revealing the diversity in architecture and application.
Provided insights on suitable models for specific research objectives in molecular science.

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Survey classifies and compares models combining GNNs and LLMs for molecular applications, suggesting guidance for future research.

Cite This Study

Segura-Alabart et al. (2026) studied this question.

synapsesocial.com/papers/6a323d93d50b63ecad20724c https://doi.org/10.1093/bioinformatics/btag387

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A survey of models composed of Graph Neural Networks and Large Language Models for Molecular Science

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Discussion

Implication

Cite This Study

A survey of models composed of Graph Neural Networks and Large Language Models for Molecular Science

Key Points

Discussion

Implication

Cite This Study

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