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Cited by 395Computational Drug Discovery MethodsMachine Learning in Materials ScienceProtein Degradation and Inhibitors

Boosting docking-based virtual screening with deep learning.

Open access
Pereira, Janaina Cruz, Caffarena, Ernesto Raúl, dos Santos, Cicero Nogueira·2016·Journal of Chemical Information and Modeling
DOI 10.1021/acs.jcim.6b00355
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Abstract

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 Vina1.1.2, 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 the DUD.

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  • Recently, DL has been applied for pose/affinity prediction and active/inactive detection, exhibiting outstanding performance compared with several well-performing scoring functions developed with both linear and nonlinear approaches.

    “Recently, DL has been applied for pose/affinity prediction and active/inactive detection, exhibiting an outstanding performance when compared with several well-performing scoring functions developed with both linear and nonlinear approaches (Wallach et al., 2015; Khamis et al., 2016; Pereira et al., 2016; Ragoza et al., 2017; Jiménez Luna et al., 2018; Nguyen et al., 2018).”

    From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Pereira et al., 2016

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Research works printed but not linked: 9

  1. [19](19) Unterthiner, T.; Mayr, A.; Klambauer, G.; Steijaert, M.; Wegner, J. K.; Ceulemans, H.; Hochreiter, S. Deep Learning as an Opportunity in Virtual Screening. Proceedings of the Deep Learning Workshop at NIPS. 2014. -
  2. [23](23) Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P. Convolutional Networks on Graphs for Learning Molecular Fingerprints. Advances in Neural Information Processing Systems. 2015; pp 2215-2223. -
  3. [26](26) Collobert, R.; Weston, J.; Bottou, L.; Karlen, M.; Kavukcuoglu, K.; Kuksa, P. Natural Language Processing (Almost) from Acratch. The Journal of Machine Learning Research 2011 , 12 , 2493-2537. -
  4. [27](27) Socher, R.; Huval, B.; Manning, C. D.; Ng, A. Y. Semantic Compositionality Through Recursive Matrix-Vector Spaces. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2012; pp 12011211. -
  5. [28](28) Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G. S.; Dean, J. Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems (NIPS) 2013 , -
  6. [29](29) dos Santos, C. N.; Gatti, M. Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts. COLING. 2014; pp 69-78. -
  7. [30](30) dos Santos, C. N.; Zadrozny, B. Learning Character-Level Representations for Part-of-Speech Tagging. Proceedings of the 31st International Conference on Machine Learning (ICML-14). 2014; pp 18181826. -
  8. [33](33) Bergstra, J.; Breuleux, O.; Bastien, F.; Lamblin, P.; Pascanu, R.; Desjardins, G.; Turian, J.; Warde-Farley, D.; Bengio, Y. Theano: a CPU and GPU Math Expression Compiler. Proceedings of the Python for Scientific Computing Conference (SciPy). 2010; p 3. -
  9. [40](40) Arlot, S.; Celisse, A. A Survey of CrossValidation Procedures for Model Selection. Statistics Surveys 2010 , 4 , 40-79. -

Cited by

Sorted by citations

Authors
Pereira, J. C., Caffarena, E. R., & dos Santos, C. N.
Affiliations
  • Fundação Oswaldo Cruz (BR)
  • IBM Watson, 1101 Kitchawan Rd., Yorktown Heights, New York 10598, United States
Corresponding author
  • Pereira, Janaina Cruz
  • Caffarena, Ernesto Raúl
Venue
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Published
2016-11-29
Volume
56
Issue
12
Pages
2495-2506
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
DOI
10.1021/acs.jcim.6b00355
OpenAlex
W2526925775
Cited by
395
References
38
Citations by year
2026: 21 · 2025: 38 · 2024: 40 · 2023: 68 · 2022: 52 · 2021: 55 · 2020: 48 · 2019: 39 · 2018: 21 · 2017: 13
FWCI
22.38
Type
journal article
Language
en
Open access
Locations
  • Journal of Chemical Information and Modeling (publishedVersion)
  • arXiv (Cornell University) (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University)
Funders
  • Fundação Oswaldo Cruz
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (407741/2012-7)
  • Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (E-26/111.401/2013)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
Keywords
protein-ligand complex, amino acid embeddings, feature extraction, AutoDock Vina, enrichment factor, deep neural network
MeSH terms
Algorithms, Humans, Ligands, Proteins, ROC Curve, Software, Drug Design, Neural Networks, Computer, Computer-Aided Design, Molecular Docking Simulation

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