Boosting Docking-Based Virtual Screening with Deep Learning

被引:219
|
作者
Pereira, Janaina Cruz [1 ]
Caffarena, Ernesto Raul [1 ]
dos Santos, Cicero Nogueira [2 ]
机构
[1] Fiocruz MS, 4365 Ave Brasil, BR-21040900 Rio De Janeiro, RJ, Brazil
[2] IBM Watson, 1101 Kitchawan Rd, Yorktown Hts, NY 10598 USA
关键词
SCORING FUNCTION; DRUG DISCOVERY; PREDICTION; NNSCORE; PAIRS;
D O I
10.1021/acs.jcim.6b00355
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
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.
引用
收藏
页码:2495 / 2506
页数:12
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