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Abstract.
Drug development is a time-consuming and resource-consuming problem. At the first stages of drug development, virtual screening plays an important role. Virtual screening is in silico selection of chemical compounds with potentially high required biochemical activity. To implement it, one typically needs high-performance computing resources, as well as software solutions for organizing the full loop of virtual screening. The paper presents the concept of High-Throughput Virtual Screening as a Service, which is a cloud-based virtual drug screening service based on the concept of Desktop Gridtype distributed computing. We describe three logical levels of service operation, user workflows and the principles of multi-user access. The service will be a software system with the necessary functionality to solve the relevant, computationally intensive problem of virtual screening using the almost unlimitedly scalable Desktop Grid computing resources.
Keywords:
Virtual Screening, Volunteer Computing, Desktop Grid, Cloud Computing, Desktop Grid as a Service, Virtual Screening as a Service, High-Throughput Virtual Screening as a Service.
PP. 102-113.
DOI 10.14357/20718632230311 References
1. Anderson DP. BOINC: A Platform for Volunteer Computing. Journal of Grid Computing. 2020;18:99–122. doi:10.1007/s10723-019-09497-9. 2. Ivashko E. Desktop Grid and Cloud Computing: Short Survey. In: Voevodin V, Sobolev S. (eds.) Supercomputing. RuSCDays 2021. Communications in Computer and Information Science, vol 1510. Springer, Cham; 2021. p. 445–456. doi:10.1007/978-3-030-92864-3_34. 3. Ivashko E. Desktop Grid as a Service Concept. In: Voevodin V, Sobolev S, Yakobovskiy M, Shagaliev R. (eds.) Supercomputing. RuSCDays 2022. Lecture Notes in Computer Science v. 13708. Springer, Cham; 2022. p. 632–643. doi:10.1007/978-3-031-22941-1_46. 4. Bechelane Maia EH, Assis LC, Alves de Oliveira T, Marques da Silva A, Gutterres Taranto A. Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Frontiers in Chemistry. 2020;8:343. doi:10.3389/fchem.2020.00343. 5. Torres PHM, Sodero ACR, Jofily P, Silva-Jr. FP. Key Topics in Molecular Docking for Drug Design. International Journal of Molecular Sciences. 2019;20(18):4574. doi:10.3390/ijms20184574. 6. Olğaç A, Türe A, Olğaç S, Möller S. Cloud-Based High Throughput Virtual Screening in Novel Drug Discovery. In: Kołodziej J, González-Vélez H. (eds.) High- Performance Modelling and Simulation for Big Data Applications. Lecture Notes in Computer Science v. 11400. Springer, Cham; 2019. p. 250-278. doi:10.1007/978-3-030-16272-6_9. 7. Singh N, Chaput L, Villoutreix BO Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Briefings in Bioinformatics. 2021;22(2):1790–1818. doi:10.1093/bib/bbaa034 8. Zhang B, Li H, Yu K, Jin Z. Molecular docking-based computational platform for high-throughput virtual screening. CCF Trans High Perform Comput. 2022;4(1):63-74. doi:10.1007/s42514-021-00086-5. 9. Mo Q, Xu Z, Yan H, Chen P, Lu Y. VSTH: a user-friendly web server for structure-based virtual screening on Tianhe-2. Bioinformatics. 2023;39(1):btac740. doi:10.1093/bioinformatics/btac740. 10. Nikitina N, Manzyuk M, Podlipnik Č, Jukic M. Volunteer computing project SiDock@home for virtual drug screening against SARS-CoV-2. In: Byrski A, Czachórski T, Gelenbe E, Grochla K, Murayama Y. (eds.) Computer Science Protecting Human Society Against Epidemics. ANTICOVID 2021. IFIP Advances in Information and Communication Technology 2021 v. 616. Springer, Cham. p. 23-34. doi:10.1007/978-3-030-86582-5_3. 11. Mottin M, de Paula Sousa BK, de Moraes Roso Mesquita NC, et al. Discovery of New Zika Protease and Polymerase Inhibitors through the Open Science Collaboration Project OpenZika. Journal of Chemical Information and Modeling. 2022;62(24):6825-6843. doi:10.1021/acs.jcim.2c00596. 12. Zhang B, D’Erasmo MP, Murelli RP, Gallicchio E. Free energy-based virtual screening and optimization of RNase H inhibitors of HIV-1 reverse transcriptase. ACS omega. 2016;1(3):435-447. doi:10.1021/acsomega.6b00123. 13. Ivashko E.E., Nikitina N.N., Möller S. High-performance virtual screening in a BOINC-based Enterprise Desktop Grid. Bulletin of the South Ural State University, Series “Computational Mathematics and Software Engineering”. 2015;4(1):57–63 (in Russ). doi:10.14529/cmse150105 14. Lloyd DG, Golfis G, Knox AJ, et al. Oncology exploration: charting cancer medicinal chemistry space. Drug discovery today. 2006;11(3-4):149-159. doi:10.1016/S1359-6446(05)03688-3. 15. Krasoulis A, Antonopoulos N, Pitsikalis V, Theodorakis S. DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features. Journal of Chemical Information and Modeling. 2022;62:4642–4659. doi:10.1021/acs.jcim.2c01057. 16. Hawkins P. Virtual Screening At Ultra-Large Scale: 1.5 Billion And Counting - Webinars [Digital resource] http://www.healthtech.com/openeye-scientific-virtualscreening-at-ultra-large-scale/ (Accessed by: 18.01.2023) 17. Blaze Cloud from Cresset. [Digital resource] www.cresset-group.com/products/blaze/#blaze-cloud (Accessed by: 18.01.2023). 18. Ivashko V, Ivashko E. BOINC-Based Volunteer Computing Projects: Dynamics and Statistics. In: Voevodin V, Sobolev S, Yakobovskiy M, Shagaliev R. (eds.) Supercomputing. RuSCDays 2022. Lecture Notes in Computer Science, v. 13708. Springer, Cham; 2022. p. 619–631 doi:10.1007/978-3-031-22941-1_45. 19. Tingting L, Lu D, Zhang H, et al. Applying highperformance computing in drug discovery and molecular simulation. National Science Review. 2016;3(1): 49-63. doi:10.1093/nsr/nww003. 20. Nikitina N, Ivashko E, Tchernykh A. Congestion Game Scheduling for Virtual Drug Screening Optimization. Journal of Computer-Aided Molecular Design. 2018:32(2);363-374. doi:10.1007/s10822-017-0093-7 . 21. Nikitina N, Ivashko E. Optimization of the Workflow in a BOINC-Based Desktop Grid for Virtual Drug Screening. In: Voevodin V., Sobolev S., Yakobovskiy M., Shagaliev R. (eds.) Supercomputing. RuSCDays 2022. Lecture Notes in Computer Science, v. 13708. Springer, Cham; 2022. p. 686–698. doi:10.1007/978-3-031-22941-1_50 22. Mazalov VV, Nikitina NN, Ivashko EE. Hierarchical Two-Level Game Model for Tasks Scheduling in a Desktop Grid. In: 2014 6th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), St. Petersburg, Russia; 2014. p. 541-545 doi:10.1109/ICUMT.2014.7002159.23. Ivashko E, Litovchenko V. Project Progress Forecasting in a Desktop Grid. In: Voevodin V, Sobolev S. (eds.) Supercomputing. RuSCDays 2021. Communications in Computer and Information Science, v. 1510. Springer, Cham; 2021. p. 487–497. doi:10.1007/978-3-030-92864-3_37. 24. Ivashko E, Nikitina N. Replication of “Tail” Computations in a Desktop Grid Project. In: Voevodin V, Sobolev S. (eds.) Supercomputing. RuSCDays 2020. Communications in Computer and Information Science, v.1331. Springer, Cham; 2020. doi:10.1007/978-3-030-64616-5_52. 25. Chernov I, Nikitina N. Virtual Screening in a Desktop Grid: Replication and the Optimal Quorum. In: Malyshkin V. (eds.) Parallel Computing Technologies. PaCT 2015. Lecture Notes in Computer Science, v. 9251. Springer, Cham; 2015. p. 258-267. doi:10.1007/978-3-319-21909-7_25. 26. Berman HM, Westbrook J, Feng Z, et al. The Protein Data Bank. Nucleic Acids Research. 2000;28:235-242. doi: 10.1093/nar/28.1.235.
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