Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges due to their reliance on time-consuming precomputed features and the heavily imbalanced nature of binding site data without further investigations on their utility in drug discovery. To address these limitations, we introduced Multiview-ATPBind and ResiBoost. Multiview-ATPBind is an end-to-end deep learning model that integrates one-dimensional (1D) sequence and three-dimensional (3D) structural information for rapid and precise residue-level pocket-ligand interaction predictions. Additionally, ResiBoost is a novel residue-level boosting algorithm designed to mitigate data imbalance by enhancing the prediction of rare positive binding residues. Our approach outperforms state-of-the-art models on benchmark data sets, showing significant improvements in balanced metrics with both experimental and AI-predicted structures. Furthermore, our model seamlessly transfers to predicting binding sites and enhancing docking simulations for kinase inhibitors, including imatinib and dasatinib, underscoring the potential of our method in drug discovery applications.

中文翻译：

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用于 ATP 结合位点预测和激酶抑制剂应用的残基水平多视图深度学习。


准确鉴定三磷酸腺苷 （ATP） 结合位点对于了解细胞功能和推进药物发现至关重要，尤其是在靶向激酶用于癌症治疗方面。现有方法面临重大挑战，因为它们依赖于耗时的预计算特征，并且结合位点数据严重不平衡，而无需进一步研究它们在药物发现中的效用。为了解决这些限制，我们引入了 Multiview-ATPBind 和 ResiBoost。Multiview-ATPBind 是一种端到端深度学习模型，它集成了一维 （1D） 序列和三维 （3D） 结构信息，用于快速、精确地预测残基水平口袋-配体相互作用。此外，ResiBoost 是一种新颖的残基水平提升算法，旨在通过增强对稀有阳性结合残基的预测来减轻数据不平衡。我们的方法在基准数据集上优于最先进的模型，在实验和 AI 预测结构的平衡指标方面都有了显著的改进。此外，我们的模型无缝转移到预测结合位点和增强激酶抑制剂（包括伊马替尼和达沙替尼）的对接模拟，强调了我们的方法在药物发现应用中的潜力。