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Multimodal Drug Target Binding Affinity Prediction Using Graph Local Substructure.

Xun Peng, Chunping Ouyang, Yongbin Liu

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    We developed MLSDTA, a multimodal deep learning model for drug-target binding affinity (DTA) prediction. It integrates graph and sequence data, outperforming existing methods by capturing crucial molecular substructures.

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    Area of Science:

    • Computational chemistry
    • Drug discovery
    • Bioinformatics

    Background:

    • Accurate drug-target binding affinity (DTA) prediction is crucial for efficient drug development.
    • Current deep learning methods for DTA prediction often rely solely on sequence or graph information, potentially leading to information loss (e.g., missing hydrogen atom data) or irrelevant feature inclusion.
    • Existing models may lack structured representations for molecular characteristics.

    Purpose of the Study:

    • To propose MLSDTA, a novel multimodal deep learning model for enhanced drug-target binding affinity prediction.
    • To comprehensively integrate graph and sequence information from both drugs and targets for improved DTA prediction accuracy.
    • To address limitations of existing methods by incorporating local substructural information and enhancing molecular feature representation.

    Main Methods:

    • Developed MLSDTA, a multimodal DTA prediction model integrating graph and sequence data using a cross-attention mechanism for feature fusion.
    • Implemented adaptive structure-aware pooling to generate graph representations capturing local substructural information.
    • Utilized the DropNode strategy to improve the distinctiveness of molecular representations.

    Main Results:

    • MLSDTA demonstrated superior performance compared to state-of-the-art models on two benchmark datasets for DTA prediction.
    • The multimodal approach effectively integrated diverse molecular information, leading to more accurate binding affinity predictions.
    • The incorporation of local substructures and enhanced molecular distinctions contributed to the model's improved efficacy.

    Conclusions:

    • MLSDTA offers a robust and effective approach for drug-target binding affinity prediction by leveraging multimodal data fusion.
    • The model's ability to capture local substructural information and enhance molecular distinctions represents a significant advancement in DTA prediction.
    • The findings validate the feasibility and superiority of MLSDTA, paving the way for its application in accelerating drug discovery pipelines.