Abstract
In drug discovery, an efficient modelling of the synergies between the existing drugs/compounds and their targets is crucial. The application of In-vitro methods over millions of compounds is tedious and expensive. Virtual Screening, an In-Silico (Computational) technique has become as indispensable constituent of contemporary drug design. This technique executes efficient In-Silico searches over millions of compounds and drastically reduces the time and cost involved in drug design. This work intends to develop a Virtual Screening model for cancer drugs using Deep Learning. For this three Deep Learning algorithms were implemented on the dataset and their performance measures were recorded and compared to the performance measures given by the traditional Machine Learning algorithms on the same dataset. A significant gain in the performance metrics of the model was observed when the deep Learning algorithms were used. The activity of the molecules in the GDB13 dataset was predicted using this model to identify the potential anti cancer drugs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schierz, A.C.: Virtual Screening of bioassay data. J. Cheminform. 1(1),21 (2009)
Walters, W.P., Stahl, M.T., Murcko, M.A.: Virtual Screening-an overview. Drug Discovery Today. 3(4),160–178 (1998)
Vyas, V., Jain, A., Jain, A., Gupta, A. : Virtual Screening: A Fast Tool for Drug Design. Sci. Pharm. 76(3),333–360 (2008)
Suma, V.R., Renjith, S., Ashok, S., Judy, M.V.: Analytical Study of selected classification algorithms for clinical dataset. Indian Journal of Science and Technology. 9(11) (2016)
Preeja, M.P., Soman, K.P.: Walk-based graph kernel for drug discovery: A review. Int. J. Comput. Appl. 94,1–7 (2014)
Gertrudes, J.C., Maltarollo, V.G., Silva, R.A., Oliveira, P.R., Honorio, K.M., da Silva, A.B.F.: Machine LearningTechniques and Drug Design. Curr. Med. Chem. 19(25), 4289–97 (2012)
Jayaraj, P.B., Ajay, M.K., Nufail, M., Gopakumar, G., Jaleel, U.C.A.: GPURFSCREEN: a GPU based virtual screening tool using random forest classifier. J. Cheminform. 8(1),12 (2016)
Unterthiner, T., Mayr, A., Klambauer, G., Steijaert, M., Wegner, J.K., Ceulemans, H., Hochreiter, S.: Deep Learning as an Opportunity in Virtual Screening. In: Proceedings of the Deep Learning Workshop at NIPS. 27,1–9 (2014)
Eckert, H., Bajorath, J.: Molecular similoarity analysis in virtual screening: foundations, limitations and novel approaches. Drug discovery today. 12(5–6),225–233 (2007)
Liu, K., Feng, J., Young, S.S.: PowerMV: a software environment for molecular viewing, descriptor generation, data analysis and hit evaluation. J. Chem. Inf. Model. 45(2),515–522 (2005)
Blum, L.C., Reymond, J.: 970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13. J. Am. Chem. Soc. 131(25),8732–8733 (2009)
O’Boyle, N.M., Banck, M., James, C.A., Morley, C., Vandermeerch, T., Hutchison, G.R.: Open Babel: An Open Chemical Toolbox. J. Cheminform. 3,33 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Leya, S., Kumar, P.N. (2020). Virtual Screening of Anticancer Drugs Using Deep Learning. In: Smys, S., Iliyasu, A.M., Bestak, R., Shi, F. (eds) New Trends in Computational Vision and Bio-inspired Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-41862-5_131
Download citation
DOI: https://doi.org/10.1007/978-3-030-41862-5_131
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41861-8
Online ISBN: 978-3-030-41862-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)