In silico prediction of drug-target interactions (DTI) is significant for drug discovery because it can largely reduce timelines and costs in the drug development process. Specifically, deep learning-based DTI approaches have been shown promising results in terms of accuracy and low cost for the prediction. However, they pay little attention to the interpretability of their prediction results and feature-level interactions between a drug and a target. In this study, we propose a novel interpretable framework that can provide reasonable cues for the interaction sites. To this end, we elaborately design a gated cross-attention mechanism that crossly attends drug and target features by constructing explicit interactions between these features. The gating function in the method enables neural models to focus on salient regions over entire sequences of drugs and proteins, and the byproduct from the function, which is the attention map, could serve as interpretable factors. The experimental results show the efficacy of the proposed method in two DTI datasets. Additionally, we show that gated cross-attention can sensitively react to the mutation, and this result could provide insights into the identification of novel drugs targeting mutant proteins.

中文翻译：

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具有门控交叉注意的药物-靶标相互作用的可解释框架

药物靶点相互作用 (DTI) 的计算机模拟预测对于药物发现具有重要意义，因为它可以在很大程度上减少药物开发过程中的时间和成本。具体而言，基于深度学习的 DTI 方法在预测的准确性和低成本方面已显示出有希望的结果。然而，他们很少关注预测结果的可解释性以及药物与目标之间的特征级相互作用。在这项研究中，我们提出了一个新颖的可解释框架，可以为交互站点提供合理的线索。为此，我们精心设计了一个门控交叉注意机制，通过构建这些特征之间的显式交互来交叉关注药物和目标特征。该方法中的门控功能使神经模型能够专注于整个药物和蛋白质序列的显着区域，而该功能的副产品，即注意力图，可以作为可解释的因素。实验结果表明了该方法在两个 DTI 数据集上的有效性。此外，我们表明门控交叉注意力可以对突变做出敏感反应，这一结果可以为鉴定靶向突变蛋白的新型药物提供见解。