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  3. Hqmol: Hierarchical Fusion And Query-guided Alignment For Molecular Graph-language Modeling.
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  3. Hqmol: Hierarchical Fusion And Query-guided Alignment For Molecular Graph-language Modeling.

Related Experiment Video

Modeling Ligands into Maps Derived from Electron Cryomicroscopy
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HQMol: Hierarchical Fusion and Query-Guided Alignment for Molecular Graph-Language Modeling.

Jiawei He1, Fei Guo1, Junwen Duan1

  • 1Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.

Journal of Chemical Information and Modeling
|June 10, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces HQMol, a new method for Large Molecular Graph-Language Models (LMGLMs) that effectively uses molecules' hierarchical structure. HQMol improves cross-modal understanding and generation by fusing hierarchical information and using query-guided alignment.

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Area of Science:

  • Artificial Intelligence
  • Computational Chemistry
  • Natural Language Processing

Background:

  • Large Molecular Graph-Language Models (LMGLMs) face challenges in aligning molecular graph and text data.
  • Existing methods often ignore the inherent hierarchical structure of molecules, limiting their ability to capture complex dependencies.
  • Current approaches may use simple concatenation for hierarchical representations and require separate contrastive learning stages for cross-modal alignment.

Purpose of the Study:

  • To address the limitations of existing LMGLMs by proposing a novel framework, HQMol.
  • To effectively model both local and global molecular semantics by incorporating hierarchical molecular structures.
  • To enhance cross-modal alignment between molecular graphs and text.

Main Methods:

  • Developed HQMol, a framework featuring a hierarchical fusion graph encoder for unified local and global molecular semantics.
  • Introduced a query-guided graph projector to bridge the semantic gap between molecular graphs and text.
  • Evaluated the framework on multiple molecule-language benchmarks.

Main Results:

  • HQMol significantly enhances model performance in molecule-language tasks.
  • The hierarchical fusion architecture effectively models molecular semantics across different levels.
  • The query-guided alignment strategy improves cross-modal understanding and generation.

Conclusions:

  • HQMol offers an effective solution for molecular graph-language modeling by leveraging hierarchical structures.
  • The proposed methods advance cross-modal understanding and generation in the molecular domain.
  • HQMol demonstrates superior performance compared to existing approaches on benchmark datasets.