A Novel Customized Big Data Analytics Framework for Drug Discovery
DOI:
https://doi.org/10.13052/2245-1439.7111Keywords:
Big Data, Analytics, Framework, Drug DiscoveryAbstract
Drug discovery is related to analytics as the method requires a technique to handle the extremely large volume of structured and unstructured biomedical data of multi-dimensional and complexity from pharmaceutical companies. To tackle the complexity of data and to get better insight into the data, big data analytics can be used to integrate the massive amount of pharmaceutical data and computational tools in an analytic framework. This paper presents an overview of big data analytics in the field of drug discovery and outlines an analytic framework which can be applied to computational drug discovery process and briefly discuss the challenges. Hence, big data analytics may contribute to better drug discovery.
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DiMasi, J. A., Feldman, L., Seckler, A.,and Wilson, A. (2010). Trends in Risks Associated With New Drug Development: Success Rates for Investigational Drugs. Clinical Pharmacology & Therapeutics. 87, 272–277.
Yousefi, N., Mehralian, G., Rasekh, H. R., and Yousefi, M. (2017). New Product Development in the Pharmaceutical Industry: Evidence from a generic market. Iranian Journal of Pharmaceutical Research?: IJPR. 16(2), 834–846.
Schmidt, Bertil, and Andreas Hildebrandt. (2017). Next-Generation Sequencing: Big Data Meets High-Performance Computing. Drug Discovery Today. 22, 712–717.
Lusher, S. J., Mcguire, R., Schaik, R. C., Nicholson, C. D., and Vlieg, J. D. (2014). Data-driven medicinal chemistry in the era of big data. Drug Discovery Today. 19(7), 859–868.
Fathima, A.J., Murugaboopathi, G., and Selvam, P. (2017). Computational Approaches in Drug Discovery: An Overview. International Journal of Advanced Research in Science and Engineering. 6(7), 189–195.
Lusher, S. J., Mcguire, R., Schaik, R. C., Nicholson, C. D., and Vlieg, J. D. (2014). Data-driven medicinal chemistry in the era of big data. Drug Discovery Today, 19(7), 859–868.
Hung, C., and Chen, C. (2014). Computational Approaches for Drug Discovery. Drug Development Research. 75(6), 412–418.
Fathima, A., Murugaboopathi, G., and Selvam, P. (2018). Pharmacophore Mapping of ligand-based virtual screening, molecular docking and molecular dynamics simulation studies for finding potent NS2B/NS3 Protease Inhibitors as potential anti-dengue drug compounds. Current Bioinformatics, 13, doi:10.2174/1574893613666180118105659
Babaie-Kafaki, S. (2016). Computational Approaches to Large-Scale Unconstrained Optimization. Studies in Big Data Big Data Optimization: Recent Developments and Challenges. 18, 391–417.
Berman, H., Nakamura, H. and Henrick, K. (2005). The Protein Data Bank (PDB) and the Worldwide PDB. In Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics eds L. B. Jorde, P. F. Little, M. J. Dunn and S. Subramaniam. doi:10.1002/047001153X.g406303
Wencong L. (2010). Data Mining and Discovery of Chemical Knowledge. In: Gaber M. eds Scientific Data Mining and Knowledge Discovery. Springer, Berlin, Heidelberg. p. 269–317.
Shi L.M., Tong W.D. (2003). Data Mining: An Integrated Approach for Drug Discovery. In: Xing WL., Cheng J. eds Biochips. Biological and Medical Physics Series. Springer, Berlin, Heidelberg. p. 71–89.
Wild, D. J. 2009. Mining large heterogeneous data sets in drug discovery. Expert Opinion on Drug Discovery. 4(10), 995–1004.
Bryant, S. D. and Langer, T. (2013). Data Mining Using Ligand Profiling and Target Fishing. In Data Mining in Drug Discovery eds R. D. Hoffmann, A. Gohier and P. Pospisil. doi:10.1002/9783527655984.ch11
Yongliang Yang, S. James Adelstein, Amin I. Kassis. (2009). Target discovery from data mining approaches, Drug Discovery Today, 14(3), 147–154.
Zhang, L., Tan, J., Han, D., and Zhu, H. (2017). From machine learning to deep learning: Progress in machine intelligence for rational drug discovery. Drug Discovery Today, 22(11), 1680–1685.
Hecht, David. (2010). Applications of machine learning and computational intelligence to drug discovery and development. Drug Development Research. 72(1), 53–65.
Karthikeyan M., Vyas R. (2014). Machine Learning Methods in Chemoinformatics for Drug Discovery. In: Practical Chemoinformatics. Springer, New Delhi. P. 133–194.
Jorissen, R. N., and Gilson, M. K. (2005). Virtual Screening of Molecular Databases Using a Support Vector Machine. J. Chem. Inf. Model. 45(3), 549–561.
Barrett S.J., Langdon W.B. (2006). Advances in the Application of Machine Learning Techniques in Drug Discovery, Design, and Development. In: Tiwari A., Roy R., Knowles J., Avineri E., Dahal K. (eds) Applications of Soft Computing. Advances in Intelligent and Soft Computing, vol. 36. Springer, Berlin, Heidelberg.
Raghupathi, W., and Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2, 3. doi:10.1186/2047-2501-2-3
Oussous, A., Benjelloun, F., Lahcen, A. A., and Belfkih, S. (2017). Big Data technologies: A survey. Journal of King Saud University – Computer and Information Sciences. doi:10.1016/j.jksuci.2017.06.001
Watson, C. (2004). New techniques and strategies in predictive ADME–Tox. Drug Discovery Today: BIOSILICO. 2(2), 55–56.
Roberts, B. R. (2000). Screening informatics: Adding value with meta-data structures and visualization tools. Drug Discovery Today, 5, 10–14.
Minna Allarakhia (2018) Evolving models of collaborative drug discovery: managing intellectual capital assets, Expert Opinion on Drug Discovery, doi: 10.1080/17460441.2018.1455659
Maia, E. H., Campos, V. A., Santos, B. D., Costa, M. S., Lima, I. G., Greco, S. J., and Taranto, A. G. (2017). Octopus: A platform for the virtual high-throughput screening of a pool of compounds against a set of molecular targets. Journal of Molecular Modeling, 23(1), 26. doi:10.1007/s00894-016-3184-9
Klein K., Kriege N., Mutzel P. (2013) Scaffold Hunter: Facilitating Drug Discovery by Visual Analysis of Chemical Space. In: Csurka G., Kraus M., Laramee R.S., Richard P., Braz J. (eds) Computer Vision, Imaging and Computer Graphics. Theory and Application. Communications in Computer and Information Science, vol. 359. Springer, Berlin, Heidelberg p. 176–192.
Baba, N., and Akaho, E. 2011. VSDK: Virtual screening of small molecules using AutoDockVina on Windows platform. Bioinformation, 6(10), 387–388.
Al-Lazikani, B. (2004). Rule of Five (Lipinski Rule of Five). Dictionary of Bioinformatics and Computational Biology. doi:10.1002/97804716501 26.dob1075
Sun, H. 2005. A Naive Bayes Classifier for Prediction of Multidrug Resistance Reversal Activity on the Basis of Atom Typing. Journal of Medicinal Chemistry, 48(12), 4031–4039.
Singh, D., and Reddy, C. K. (2014). A survey on platforms for big data analytics. Journal of Big Data, 2(1). doi:10.1186/s40537-014-0008-6