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

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ID: 317328
2026
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Abstract
Abstract Motivation Graphs have been demonstrated to have an impressive ability to keep the structural and semantic properties of chemical compounds and are therefore widely used in molecular modeling. Moreover, Graph Neural Networks (GNNs) and Large Language Models (LLMs) have achieved outstanding results in predicting molecular properties and generating textual descriptions given graphs or texts, respectively. Recently, some mathematical models have been presented for molecular science applications, which combine the GNNs ability to capture the structural and semantic information and the generative ability of LLMs. However, these models are dispersed across the literature and vary in architecture, input–output design, and application scope, making it difficult to systematically compare them or to identify suitable approaches for specific research objectives. Results This paper classifies recent GNN–LLM models and summarizes them with the aim of providing guidance for research involving their use or the development of new models. We present tables that depict the properties and parameters of the models, as well as their associated chemical computational applications. In addition, a new model classification is presented to help researchers define the models they use or future ones. Finally, we quantitatively compare these models based on their reported experimental results.
Reference Key
openalex_W7164842051 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Natàlia Segura-Alabart, Francesc Serratosa
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag387
URL
https://doi.org/10.1093/bioinformatics/btag387
Keywords Keywords not found

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