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.

本文介绍了一种基于深度学习的虚拟筛选方法，使用原子和氨基酸嵌入来提取蛋白质 - 配体复合物的相关特征，并利用一种卷积层处理化合物作为原子上下文的分散向量表示以提高其中的性能。通过在 DUD 中使用 AutoDockVina1.1.2 和 Dock6.6 的输出进行严格评估，本文方法在 AUC ROC 和富集因子方面优于两个对照组，并在 DUD 使用 40 个受体的情况下实现了最好的 AUC ROC 值 0.81。