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  3. Gero-llm: A Multimodal Large Language Model For Geroprotector Discovery Via Cross-modal Differentiated Mutual Learning.
  1. Home
  3. Gero-llm: A Multimodal Large Language Model For Geroprotector Discovery Via Cross-modal Differentiated Mutual Learning.

Related Experiment Video

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Gero-LLM: A Multimodal Large Language Model for Geroprotector Discovery via Cross-Modal Differentiated Mutual

Zhongshen Li, Jixiang Yu, Shen You

    IEEE Journal of Biomedical and Health Informatics
    |April 21, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Gero-LLM, a novel multimodal framework, accelerates the discovery of geroprotectors by integrating language models and graph networks. This approach enhances predictions for aging interventions, overcoming data limitations in drug discovery.

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    Area of Science:

    • Biogerontology
    • Computational Chemistry
    • Artificial Intelligence in Drug Discovery

    Background:

    • Geroprotectors are key to intervening in aging pathologies and extending lifespan.
    • Current geroprotector discovery faces challenges due to data quality and pathway redundancy.
    • Existing methods using single data modalities struggle with complex structure-activity relationships.

    Purpose of the Study:

    • To introduce Gero-LLM, a multimodal framework for enhanced geroprotector discovery.
    • To synergize large language models (LLMs) with Graph Isomorphism Network with Edge features (GINE) for improved predictive ability.
    • To address limitations in standard fine-tuning using a cross-modal differentiated deep mutual learning (CM-Diff-DML) strategy.

    Main Methods:

    • Developed Gero-LLM, a multimodal framework combining LLMs and GINE for chemical data analysis.
  • Fused textual and structural chemical information using multimodal embeddings.
  • Employed CM-Diff-DML to enforce modality diversity and prevent mode collapse during training.

    • Main Results:

    • Gero-LLM achieved state-of-the-art performance in geroprotector discovery.
    • Demonstrated robustness on imbalanced datasets, simulating real-world screening.
    • In silico mutagenesis confirmed Gero-LLM's ability to identify key chemical pharmacophores.

    Conclusions:

    • Gero-LLM offers a robust platform for accelerating the discovery of aging interventions.
    • The framework effectively bridges LLMs with multimodal molecular information.
    • This approach enhances the predictive power for identifying novel geroprotective compounds.