Identifying drug-target interactions (DTIs) is essential for drug discovery and development. Existing deep learning approaches to DTI prediction often employ powerful feature encoders to represent drugs and targets holistically, which usually cause significant redundancy and noise by neglecting the restricted binding regions. Furthermore, many previous DTI networks ignore or simplify the complex intermolecular interaction process involving diverse binding types, which significantly limits both predictive ability and interpretability. We propose ReduMixDTI, an end-to-end model that addresses feature redundancy and explicitly captures complex local interactions for DTI prediction. In this study, drug and target features are encoded by using graph neural networks and convolutional neural networks, respectively. These features are refined from channel and spatial perspectives to enhance the representations. The proposed attention mechanism explicitly models pairwise interactions between drug and target substructures, improving the model's understanding of binding processes. In extensive comparisons with seven state-of-the-art methods, ReduMixDTI demonstrates superior performance across three benchmark data sets and external test sets reflecting real-world scenarios. Additionally, we perform comprehensive ablation studies and visualize protein attention weights to enhance the interpretability. The results confirm that ReduMixDTI serves as a robust and interpretable model for reducing feature redundancy, contributing to advances in DTI prediction.

中文翻译：

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ReduMixDTI：通过特征冗余减少和可解释注意力机制预测药物-靶点相互作用。


识别药物-靶标相互作用 （DTI） 对于药物发现和开发至关重要。现有的深度学习 DTI 预测方法通常采用强大的特征编码器来整体表示药物和靶标，这通常会因忽略受限的结合区域而导致严重的冗余和噪声。此外，许多以前的 DTI 网络忽略或简化了涉及不同结合类型的复杂分子间相互作用过程，这极大地限制了预测能力和可解释性。我们提出了 ReduMixDTI，这是一种端到端模型，用于解决特征冗余问题并显式捕获复杂的局部交互以进行 DTI 预测。在本研究中，药物和靶点特征分别使用图神经网络和卷积神经网络进行编码。这些特征从通道和空间角度进行优化，以增强表示。提出的注意力机制明确模拟了药物和靶点子结构之间的成对相互作用，从而提高了模型对结合过程的理解。在与七种最先进方法的广泛比较中，ReduMixDTI 在三个基准数据集和反映真实场景的外部测试集中表现出卓越的性能。此外，我们进行全面的消融研究并可视化蛋白质注意力权重以提高可解释性。结果证实，ReduMixDTI 是一个健壮且可解释的模型，用于减少特征冗余，有助于 DTI 预测的进步。