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Updated: Sep 9, 2025

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MOSAIC: A Multi-Granularity Cross-Modal Framework for Predicting Synergistic Drug Combinations in Personalized

Licai Zhang, Xiao Kang, Xinxing Yang

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    |September 4, 2025
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    Summary
    This summary is machine-generated.

    This study introduces MOSAIC, an AI method predicting synergistic drug combinations for personalized cancer therapy by analyzing molecular and fragment-level features. MOSAIC enhances drug discovery and treatment planning by identifying key synergistic fragments.

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    Area of Science:

    • Computational chemistry
    • Artificial intelligence in drug discovery
    • Personalized medicine

    Background:

    • Personalized cancer treatment relies on effective drug combinations.
    • Computational methods are crucial for identifying synergistic drug pairs.
    • Existing methods lack fine-grained molecular interaction analysis.

    Purpose of the Study:

    • To develop an AI-driven method for predicting synergistic drug combinations.
    • To improve personalized cancer treatment by considering multi-granularity molecular features.
    • To address limitations in current computational approaches for drug synergy prediction.

    Main Methods:

    • MOSAIC utilizes a dual-layer representation system with graph and SMILES modalities.
    • BRICS algorithm decomposes molecules into fragments; bidirectional cross-attention enables information exchange.
    • Contrastive learning ensures semantic consistency; bilinear and multi-head attention capture fragment interactions.

    Main Results:

    • MOSAIC outperforms state-of-the-art methods in predicting synergistic drug combinations.
    • Literature validation confirms predicted combinations align with clinical evidence.
    • Visualization analysis identifies critical molecular fragments for drug synergy.

    Conclusions:

    • MOSAIC offers a novel approach to personalized synergistic drug combination prediction.
    • The method provides insights for optimizing cancer treatment strategies.
    • MOSAIC aids in identifying key molecular fragments crucial for therapeutic synergy.