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Drug-target binding affinity prediction model based on multi-scale diffusion and interactive learning
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DOI：10.1016/j.eswa.2024.124647.png)
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摘要
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Drug-target interactions (DTIs) play a key role in drug discovery and development as they are critical in understanding the complex mechanisms of underlying drugs and their corresponding targets. Unfortunately, some studies do not fully recognize the significant contribution of graph structure to drug feature extraction, thereby neglecting the extraction of local and global structures inherent in molecular graphs. Furthermore, most existing drug-target binding affinity (DTA) prediction models ignore or simplify complex interaction mechanisms, thus compromising the predictive power of the models. Therefore, this paper proposes a novel DTA prediction model utilizing multi-scale diffusion and interactive learning (MDCT-DTA). To address the limitations of current methods, the multi-scale graph diffusion convolution (MGDC) module is introduced, which can effectively capture the complex interactions between drug molecular graph nodes. Furthermore, a CNN-Transformer Network (CTN) block is proposed to capture the interactions and interdependencies between different amino acids, thereby enhancing the representation and learning capabilities of the model. In addition, a local inter-layer information interaction structure is designed to specifically explore the relationship between drug features and protein features, so as to improve the representativeness and robustness of the model's structural features. The performance of the proposed model was evaluated using four publicly available benchmark datasets, Davis, KIBA, BindingDB, and Metz. The experimental results confirm that the proposed model can more accurately predict the binding affinity between a drug and its targets. They also provide a new perspective for the prediction task of DTA and contribute to the overall development and improvement of DTA prediction models.
Keyword:
Deep learning
Graph neural network
Diffusion
Transformer
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
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