In this work, we propose an approach based on deep convolutional neural networks to improve docking-based virtual screening. The method uses docking simulation results as input to a deep neural network, DeepVS from now on, which automatically learns to extract relevant features from basic data such as compound atom types, atomic partial charges, and the distance between atoms. DeepVS learns abstract features that are suitable to discriminate between active ligands and decoys in a protein-compound complex. To the best of our knowledge, this work is the first on using deep learning to improve docking-based virtual screening. Recent works on improving docking-based virtual screening have only used traditional shallow neural networks with human defined features. We evaluated DeepVS on the Directory of Useful Decoys (DUD), which contains 40 different receptors. In our experiments we used the output of two docking programs: AutodockVina1.1.2 and Dock6.6. DeepVS outperformed the docking programs in both AUC ROC and enrichment factor. Moreover, when compared to the results of other systems previously reported in the literature, DeepVS achieved the state-of-the-art AUC of 0.81. The main contributions of this work are: (1) Proposition of a new deep learning-based approach that achieves state-of-the-art performance on docking-based virtual screening; (2) Introduction of the concept of atom and amino acid embeddings, which can also be used in other deep learning applications for computational biology; (3) Proposition of an effective method to create distributed vector representations of protein-compound complexes that models the compound as a set of atom contexts that is further processed by a convolutional layer.

Improving docking-based virtual screening with convolutional neural networks

256th National Meeting (2018)

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