Virtual screening: A challenge for deep learning

Advances in Intelligent Systems and Computing

Volumn 477, Issue , 2016, Pages 13-22

  Pérez Sianes, Javier ^a   Pérez Sánchez, Horacio ^b   Díaz, Fernando ^a  

^a UNIVERSITY OF VALLADOLID   (Spain)

^b CATHOLIC UNIVERSITY OF MURCIA   (Spain)

Author keywords

Deep Learning; Drug discovery; Ligand based; Machine Learning; Structure based; Virtual Screening

Indexed keywords

BIOINFORMATICS; E-LEARNING; LEARNING SYSTEMS;

COMPUTER AIDED; DE FACTO STANDARD; DRUG DISCOVERY; DRUG SCREENING; LEARNING FIELDS; STRUCTURE-BASED; VIRTUAL SCREENING;

DEEP LEARNING;

EID: 84976394354     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-40126-3_2     Document Type: Conference Paper

References (27)

1. Lionta, E., Spyrou, G., Vassilatis, D., Cournia, Z.: Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances. Curr. Top. Med. Chem. 14, 1923-1938 (2014)
2. Ripphausen, P., Nisius, B., Bajorath, J.: State-of-the-art in ligand-based virtual screening. Drug Discov. Today 16, 372-376 (2011)
3. Gertrudes, J.C., Maltarollo, V.G., Silva, R.A., Oliveira, P.R., Honorio, K.M., da Silva, A.B.F.: Machine learning techniques and drug design. Curr. Med. Chem. 19, 4289-4297 (2012)
4. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436-444 (2015)
5. Bielska, E., Lucas, X., Czerwoniec, A., Kasprzak, J.M., Kaminska, K.H., Bujnicki, J.M.: Virtual screening strategies in drug design-methods and applications. Biotechnologia 92, 249-264 (2011)
6. Kitchen, D.B., Decornez, H., Furr, J.R., Bajorath, J.: Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 3, 935-949 (2004)
7. Johnson, A.M., Maggiora, G.M.: Concepts and Applications of Molecular Similarity. John Wiley & Sons Inc., New York (1990)
8. Drwal, M.N., Griffith, R.: Combination of ligand-and structure-based methods in virtual screening. Drug Discov. Today. Technol. 10, e395-e401 (2013)
9. Schneider, G., Böhm, H.J.: Virtual screening and fast automated docking methods. Drug Discov. Today 7, 64-70 (2002)
10. Kubinyi, H.: Chemogenomics in drug discovery. Ernst Schering Res. Found. Workshop, 1-19 (2006)
11. Klabunde, T.: Chemogenomic approaches to drug discovery: similar receptors bind similar ligands. Br. J. Pharmacol. 152, 5-7 (2007)
12. Flach, P.: Machine Learning: The Art and Science of Algorithms that Make Sense of Data (2012)
13. Butkiewicz, M., Mueller, R., Selic, D., Dawson, E., Meiler, J.: Application of machine learning approaches on quantitative structure activity relationships. In: 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 255-262. IEEE (2009)
14. Melville, J.L., Burke, E.K., Hirst, J.D.: Machine learning in virtual screening. Comb. Chem. High Throughput Screen. 12, 332-343 (2009)
15. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798-1828 (2013)
16. Bengio, Y.: Learning Deep Architectures for AI. Found. Trends Mach. Learn. 2, 1-127 (2009)
17. Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359-366 (1989)
18. Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527-1554 (2006)
19. Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.-A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning-ICML 2008, pp. 1096-1103. ACM Press, New York (2008)
20. Poultney, C., Chopra, S., Lecun, Y.: Efficient learning of sparse representations with an energy-based model. In: Advances in Neural Information Processing Systems (NIPS 2006), pp. 1137-1144. MIT Press (2006)
21. Kaggle-Merck Molecular Activity Challenge. https://www.kaggle.com/c/MerckActivity
22. Lusci, A., Pollastri, G., Baldi, P.: Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules. J. Chem. Inf. Model. 53, 1563-1575 (2013)
23. Xu, Y., Dai, Z., Chen, F., Gao, S., Pei, J., Lai, L.: Deep Learning for Drug-Induced Liver Injury. J. Chem. Inf. Model. (2015). 151013124508007
24. Dahl, G.E., Jaitly, N., Salakhutdinov, R.: Multi-task Neural Networks for QSAR Predictions. CoRR. abs/1406.1 (2014)
25. Ma, J., Sheridan, R.P., Liaw, A., Dahl, G.E., Svetnik, V.: Deep neural nets as a method for quantitative structure-activity relationships. J. Chem. Inf. Model. 55, 263-274 (2015)
26. Ramakrishnan, R., von Lilienfeld, O.A.: Machine Learning, Quantum Mechanics, and Chemical Compound Space (2015). http://arxiv.org/abs/1510.07512
27. Maggiora, G.M.: On outliers and activity cliffs-why QSAR often disappoints. J. Chem. Inf. Model. 46, 1535 (2006)