通过深度学习促进基于对接的虚拟放映
Boosting Docking-based Virtual Screening with Deep Learning
摘要：
在这项工作中，我们提出了一种深度学习方法来改进基于对接的虚拟筛选。引入的深度神经网络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：AutodockVina1.1.2和Dock6.6。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AutodockVina1.1.2的输出，DeepVS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。
英文摘要：
In this work, we propose a deep learning approach to improve docking-based virtual screening. The introduced deep neural network, 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: AutodockVina1.1.2 and Dock6.6. Using a strict evaluation with leave-one-out cross-validation, DeepVS outperforms the docking programs in both AUC ROC and enrichment factor. Moreover, using the output of AutodockVina1.1.2, 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 DUD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
