Boosting Docking-Based Virtual Screening with Deep Learning
Boosting Docking-Based Virtual Screening with Deep Learning
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DOI：
10.1021/acs.jcim.6b00355
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发表时间：
2016-12-01
影响因子：
5.6
通讯作者：
dos Santos, Cicero Nogueira
中科院分区：
文献类型：
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作者：
Pereira, Janaina Cruz;Caffarena, Ernesto Raul;dos Santos, Cicero Nogueira
在这项工作中，我们提出了一种深度学习方法来改善基于对接的虚拟筛选。介绍的深度神经网络DeepVS使用对接程序的输出，并学习如何从基本数据中提取相关特征，例如从蛋白质配体复合物中获得的原子和残基类型。我们的方法引入了原子和氨基酸嵌入的使用，并通过将化合物建模为一组由卷积层进一步处理的原子上下文，实现了创建蛋白质配体复合物的分布式矢量表示的有效方法。所提出的方法的主要优点之一是，它不需要特征工程。我们在有用诱饵目录（DUD）上评估DeepVS，使用两个对接程序的输出：Autodock Vina1.1.2和Dock 6.6。使用留一交叉验证的严格评估，DeepVS在AUC ROC和富集因子方面都优于对接程序。此外，使用Autodock DeepVS的输出实现了0.81的AUC ROC，据我们所知，这是迄今为止使用来自DUD的40种受体进行虚拟筛选的最佳AUC。
In this work, we propose a deep learning approach to improve docking-based virtual screening. The deep neural network that is introduced, DeepVS, uses the output of a docking program and learns how to extract relevant features from basic data such as atom and residues types obtained from protein ligand complexes. Our approach introduces the use of atom and amino acid embeddings and implements an effective way of creating distributed vector representations of protein ligand complexes by modeling the compound as a set of atom contexts that is further processed by a convolutional layer. One of the main advantages of the proposed method is that it does not require feature engineering. We evaluate DeepVS on the Directory of Useful Decoys (DUD), using the output of two docking programs: Autodock Vina1.1.2 and Dock 6.6. Using a strict evaluation with leave-one-out cross-validation, DeepVS outperforms the docking programs, with regard to both AUC ROC and enrichment factor. Moreover, using the output of Autodock DeepVS achieves, an AUC ROC of 0.81, which, to the best of our knowledge, is the best AUC reported so far for virtual screening using the 40 receptors from the DUD.