Congestion Game Scheduling Implementation for High-Throughput Virtual Drug Screening Using BOINC-Based Desktop Grid
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.
KeywordsDrug discovery Virtual drug screening High-performance computing High-throughput computing Desktop grid BOINC Scheduling Game theory Congestion game
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.
- 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
- 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
- 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
- 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
- 22.Downs, G.M., Barnard, J.M.: Clustering methods and their uses in computational chemistry. Rev. Comput. Chem. 18, 1–40 (2003)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
- 30.Ieong, S. et al.: Fast and compact: a simple class of congestion games. In: Proceedings of AIII, pp. 1–6 (2005)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