Accurate identification of compound-protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and BindingDB, as well as of the known drug-target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 receptor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI.

中文翻译：

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DeepCPI：一种基于深度学习的框架，用于大规模计算机药物筛查。

在计算机上准确识别化合物-蛋白质相互作用（CPI）可能加深我们对药物作用的潜在机制的了解，从而显着促进药物的发现和开发。常规的基于相似度或对接的预测CPI的计算方法很少利用当前可用的大规模未标记化合物和蛋白质数据中的潜在特征，并且经常将其使用范围限制在相对较小的数据集中。在本研究中，我们提出DeepCPI，这是一种新颖的通用且可扩展的计算框架，该框架将有效的特征嵌入（一种表示学习的技术）与强大的深度学习方法相结合，可准确地大规模预测CPI。DeepCPI会从大量未标记的数据中自动学习化合物和蛋白质的隐式但具有表达力的低维特征。对大型数据库（例如ChEMBL和BindingDB）中测得的CPI的评估以及对DrugBank已知的药物-靶标相互作用的评估，证明了DeepCPI的出色预测性能。此外，实验验证了使用DeepCPI预测的小分子化合物与三个G蛋白偶联受体靶标（胰高血糖素样肽1受体，胰高血糖素受体和血管活性肠肽受体）之间的几种相互作用。本研究表明，DeepCPI是药物发现和重新定位的有用而强大的工具。DeepCPI的源代码可以从https://github.com/FangpingWan/DeepCPI下载。对大型数据库（例如ChEMBL和BindingDB）中测得的CPI的评估以及对DrugBank已知的药物-靶标相互作用的评估，证明了DeepCPI的出色预测性能。此外，实验验证了使用DeepCPI预测的小分子化合物与三个G蛋白偶联受体靶标（胰高血糖素样肽1受体，胰高血糖素受体和血管活性肠肽受体）之间的几种相互作用。本研究表明，DeepCPI是药物发现和重新定位的有用而强大的工具。DeepCPI的源代码可以从https://github.com/FangpingWan/DeepCPI下载。对大型数据库（例如ChEMBL和BindingDB）中测得的CPI的评估以及对DrugBank已知的药物-靶标相互作用的评估，证明了DeepCPI的出色预测性能。此外，实验验证了使用DeepCPI预测的小分子化合物与三个G蛋白偶联受体靶标（胰高血糖素样肽1受体，胰高血糖素受体和血管活性肠肽受体）之间的几种相互作用。本研究表明，DeepCPI是药物发现和重新定位的有用而强大的工具。DeepCPI的源代码可以从https://github.com/FangpingWan/DeepCPI下载。展示了DeepCPI出色的预测性能。此外，实验验证了使用DeepCPI预测的小分子化合物与三个G蛋白偶联受体靶标（胰高血糖素样肽1受体，胰高血糖素受体和血管活性肠肽受体）之间的几种相互作用。本研究表明，DeepCPI是药物发现和重新定位的有用而强大的工具。DeepCPI的源代码可以从https://github.com/FangpingWan/DeepCPI下载。展示了DeepCPI出色的预测性能。此外，实验验证了使用DeepCPI预测的小分子化合物与三个G蛋白偶联受体靶标（胰高血糖素样肽1受体，胰高血糖素受体和血管活性肠肽受体）之间的几种相互作用。本研究表明，DeepCPI是药物发现和重新定位的有用而强大的工具。DeepCPI的源代码可以从https://github.com/FangpingWan/DeepCPI下载。本研究表明，DeepCPI是药物发现和重新定位的有用而强大的工具。DeepCPI的源代码可以从https://github.com/FangpingWan/DeepCPI下载。本研究表明，DeepCPI是药物发现和重新定位的有用而强大的工具。DeepCPI的源代码可以从https://github.com/FangpingWan/DeepCPI下载。