Abstract

Accurately predicting compound–protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable prediction approaches. In this study, we propose GraphsformerCPI, an end-to-end deep learning framework that improves prediction performance and interpretability. GraphsformerCPI treats compounds and proteins as sequences of nodes with spatial structures, and leverages novel structure-enhanced self-attention mechanisms to integrate semantic and graph structural features within molecules for deep molecule representations. To capture the vital association between compound atoms and protein residues, we devise a dual-attention mechanism to effectively extract relational features through .cross-mapping. By extending the powerful learning capabilities of Transformers to spatial structures and extensively utilizing attention mechanisms, our model offers strong interpretability, a significant advantage over most black-box deep learning methods. To evaluate GraphsformerCPI, extensive experiments were conducted on benchmark datasets including human, C. elegans, Davis and KIBA datasets. We explored the impact of model depth and dropout rate on performance and compared our model against state-of-the-art baseline models. Our results demonstrate that GraphsformerCPI outperforms baseline models in classification datasets and achieves competitive performance in regression datasets. Specifically, on the human dataset, GraphsformerCPI achieves an average improvement of 1.6% in AUC, 0.5% in precision, and 5.3% in recall. On the KIBA dataset, the average improvement in Concordance index (CI) and mean squared error (MSE) is 3.3% and 7.2%, respectively. Molecular docking shows that our model provides novel insights into the intrinsic interactions and binding mechanisms. Our research holds practical significance in effectively predicting CPIs and binding affinities, identifying key atoms and residues, enhancing model interpretability.

Graphical abstract

中文翻译：

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GraphsformerCPI：用于预测化合物-蛋白质相互作用的图形转换器

摘要

准确预测化合物-蛋白质相互作用（CPI）是计算机辅助药物设计中的一项关键任务。近年来，化合物活性和生物医学数据的指数增长凸显了对高效且可解释的预测方法的需求。在这项研究中，我们提出了 GraphsformerCPI，这是一种端到端的深度学习框架，可以提高预测性能和可解释性。GraphsformerCPI 将化合物和蛋白质视为具有空间结构的节点序列，并利用新颖的结构增强自注意力机制将分子内的语义和图形结构特征集成以实现深层分子表示。为了捕捉化合物原子和蛋白质残基之间的重要关联，我们设计了一种双重注意机制，通过交叉映射有效提取关系特征。通过将 Transformers 强大的学习能力扩展到空间结构并广泛利用注意力机制，我们的模型提供了强大的可解释性，这比大多数黑盒深度学习方法具有显着优势。为了评估 GraphsformerCPI，在包括人类、线虫、Davis 和 KIBA 数据集在内的基准数据集上进行了广泛的实验。我们探讨了模型深度和辍学率对性能的影响，并将我们的模型与最先进的基线模型进行了比较。我们的结果表明，GraphsformerCPI 在分类数据集中优于基线模型，并在回归数据集中实现了有竞争力的性能。具体来说，在人类数据集上，GraphsformerCPI 的 AUC 平均提高了 1.6%，准确率平均提高了 0.5%，召回率平均提高了 5.3%。在 KIBA 数据集上，一致性指数 (CI) 和均方误差 (MSE) 的平均改进分别为 3.3% 和 7.2%。分子对接表明，我们的模型为内在相互作用和结合机制提供了新的见解。我们的研究对于有效预测 CPI 和结合亲和力、识别关键原子和残基、增强模型可解释性具有实际意义。

图形概要