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Congestion Game Scheduling Implementation for High-Throughput Virtual Drug Screening Using BOINC-Based Desktop Grid

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Parallel Computing Technologies (PaCT 2017)

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Abstract

Virtual drug screening is one of the most common applications of high-throughput computing. As virtual screening is time consuming, a problem of obtaining a diverse set of hits in a short time is very important. We propose a mathematical model based on game theory. Task scheduling for virtual drug screening in high-performance computational systems is considered as a congestion game between computing nodes to find the equilibrium solutions for best balancing between the number of interim hits and their chemical diversity. We present the developed scheduling algorithm implementation for Desktop Grid and Enterprise Desktop Grid, and perform comprehensive computational experiments to evaluate its performance. We compare the algorithm with two known heuristics used in practice and observe that game-based scheduling outperforms them by the hits discovery rate and chemical diversity at earlier steps.

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References

  1. Pharmaceutical Research and Manufacturers of America (PhRMA). Biopharmaceutical Industry Profile (2016). http://phrma.org/sites/default/files/pdf/biopharmaceutical-industry-profile.pdf accessed 2017/05/14

  2. Bielska, E., Lucas, X., Czerwoniec, A., et al.: Virtual screening strategies in drug design — methods and applications. J. Biotechnol. Comput. Biol. Bionanotechnol. 92(3), 249–264 (2011)

    Google Scholar 

  3. Bohacek, R.S., McMartin, C., Guida, W.C.: The art and practice of structure-based drug design: A molecular modeling perspective. Med. Res. Rev. 16(1), 3–50 (1996)

    Article  Google Scholar 

  4. Irwin, J., et al.: ZINC: a free tool to discover chemistry for biology. J. Chem. Inf. Model. 52, 1757–1768 (2012)

    Article  Google Scholar 

  5. Bento, A.P., et al.: The ChEMBL bioactivity database: an update. Nucleic Acids Res. 42, 1083–1090 (2014)

    Article  Google Scholar 

  6. Pence, H.E., Williams, A.: ChemSpider: an online chemical information resource. J. Chem. Educ. 87(11), 1123–1124 (2010)

    Article  Google Scholar 

  7. Bolton, E.E., et al.: Chapter 12 - PubChem: integrated platform of small molecules and biological activities. Annu. Rep. Comput. Chem. 4, 217–241 (2008). Elsevier

    Article  Google Scholar 

  8. Ruddigkeit, L., van Deursen, R., Blum, L.C., Reymond, J.-L.: Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J. Chem. Inf. Model. 52, 2864–2875 (2012)

    Article  Google Scholar 

  9. Liu, T., et al.: Applying high performance computing in drug discovery and molecular simulation. Nat. Sci. Rev. 3(1), 49–63 (2016)

    Article  MathSciNet  Google Scholar 

  10. Yasuda, S., Nogami, Y., Fukushi, M.: A dynamic job scheduling method for reliable and high-performance volunteer computing. In: 2nd International Conference on Information Science and Security (ICISS 2015), pp. 1–4. IEEE (2015)

    Google Scholar 

  11. Sonnek, J., Chandra, A., Weissman, J.: Adaptive reputation-based scheduling on unreliable distributed infrastructures. IEEE Trans. Parallel Distrib. Syst. 18(11), 1551–1564 (2007)

    Article  Google Scholar 

  12. Byun, E., et al.: MJSA: Markov job scheduler based on availability in desktop grid computing environment. Futur. Gener. Comput. Syst. 23, 616–622 (2007)

    Article  Google Scholar 

  13. Gil, J.-M., Kim, S., Lee, J.: Task scheduling scheme based on resource clustering in desktop grids. Int. J. Commun. Syst. 27(6), 918–930 (2014)

    Article  Google Scholar 

  14. Miyakoshi, Y., Watanabe, K., Fukushi, M., Nogami, Y.: A job scheduling method based on expected probability of completion of voting in volunteer computing. In: 2nd International Symposium on Computing and Networking, pp. 399–405. IEEE (2014)

    Google Scholar 

  15. Wang, Y., et al.: Toward integrity assurance of outsourced computing — a game theoretic perspective. Futur. Gener. Comput. Syst. 55, 87–100 (2016)

    Article  Google Scholar 

  16. Donassolo, B., et al.: Non-cooperative scheduling considered harmful in collaborative volunteer computing environments. In: Proceedings of 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 144–153 (2011)

    Google Scholar 

  17. Legrand, A.: Scheduling for large scale distributed computing systems: approaches and performance evaluation issues. Distrib. Parallel, Clust. Comput. [cs.DC], Université Grenoble Alpes, p. 167 (2015)

    Google Scholar 

  18. Tanrikulu, Y., Krüger, B., Proschak, E.: The holistic integration of virtual screening in drug discovery. Drug Discov. Today 18(7/8), 358–364 (2013)

    Article  Google Scholar 

  19. Lionta, E., Spyrou, G., Vassilatis, D.K., Cournia, Z.: Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr. Top. Med. Chem. 14, 1923–1938 (2014)

    Article  Google Scholar 

  20. Rupakheti, C., Virshup, A., Yang, W., Beratan, D.N.: Strategy to discover diverse optimal molecules in the small molecule universe. J. Chem. Inf. Model. 55, 529–537 (2015)

    Article  Google Scholar 

  21. Ashton, M., et al.: Identification of diverse database subsets using property-based and fragment-based molecular descriptions. Quant. Struct. Act. Relationsh. 21, 598–604 (2002)

    Article  Google Scholar 

  22. Downs, G.M., Barnard, J.M.: Clustering methods and their uses in computational chemistry. Rev. Comput. Chem. 18, 1–40 (2003)

    Google Scholar 

  23. Oprea, T.I., Gottfries, J.: Chemography: the art of navigating in chemical space. J. Comb. Chem. 3, 157–166 (2001)

    Article  Google Scholar 

  24. Nikitina, N., Ivashko, E., Tchernykh, A.: Congestion game scheduling for virtual drug screening optimization. J. Comput. Aided Mol. Des. (2017). Manuscript submitted for publication

    Google Scholar 

  25. Patterson, D.E., et al.: Neighborhood behavior: a useful concept for validation of “molecular diversity” descriptors. J. Med. Chem. 39, 3049–3059 (1996)

    Article  Google Scholar 

  26. Willet, P., Barnard, J.M., Downs, G.M.: Chemical similarity searching. J. Chem. Inf. Comput. Sci. 38(6), 983–996 (1998)

    Article  Google Scholar 

  27. Hann, M.M., Leach, A.R., Harper, G.: Molecular complexity and its impact on the probability of finding leads for drug discovery. J. Chem. Inf. Comput. Sci. 41, 856–864 (2001)

    Article  Google Scholar 

  28. Rosenthal, R.: A class of games possessing pure-strategy Nash equilibria. Int. J. Game Theor. 2(1), 65–67 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  29. Milchtaich, I.: Congestion games with player-specific payoff functions. Games Econ. Behav. 13, 111–124 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  30. Ieong, S. et al.: Fast and compact: a simple class of congestion games. In: Proceedings of AIII, pp. 1–6 (2005)

    Google Scholar 

  31. Gairing, M., Klimm, M.: Congestion games with player-specific costs revisited. In: Vöcking, B. (ed.) SAGT 2013. LNCS, vol. 8146, pp. 98–109. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41392-6_9

    Chapter  Google Scholar 

  32. Anderson, D.P.: BOINC: A system for public-resource computing and storage. In: Proceedings of 5th IEEE/ACM International Workshop on Grid Computing, pp. 4–10 (2004)

    Google Scholar 

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Acknowledgments

This work is partially supported by the Russian Fund for Basic Research under grants no. 16-07-00622 and 15-29-07974, and CONACYT (Consejo Nacional de Ciencia y Tecnología, México) under grant no. 178415.

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Correspondence to Natalia Nikitina .

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Nikitina, N., Ivashko, E., Tchernykh, A. (2017). Congestion Game Scheduling Implementation for High-Throughput Virtual Drug Screening Using BOINC-Based Desktop Grid. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2017. Lecture Notes in Computer Science(), vol 10421. Springer, Cham. https://doi.org/10.1007/978-3-319-62932-2_46

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  • DOI: https://doi.org/10.1007/978-3-319-62932-2_46

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