Version 1
: Received: 15 September 2021 / Approved: 16 September 2021 / Online: 16 September 2021 (11:02:38 CEST)
How to cite:
Chitara, D.; B. S., S. Big Data Approach to Large Scale Molecular Dynamics Simulations: Necessity and Inevitability for Drug Design. Preprints2021, 2021090275. https://doi.org/10.20944/preprints202109.0275.v1
Chitara, D.; B. S., S. Big Data Approach to Large Scale Molecular Dynamics Simulations: Necessity and Inevitability for Drug Design. Preprints 2021, 2021090275. https://doi.org/10.20944/preprints202109.0275.v1
Chitara, D.; B. S., S. Big Data Approach to Large Scale Molecular Dynamics Simulations: Necessity and Inevitability for Drug Design. Preprints2021, 2021090275. https://doi.org/10.20944/preprints202109.0275.v1
APA Style
Chitara, D., & B. S., S. (2021). Big Data Approach to Large Scale Molecular Dynamics Simulations: Necessity and Inevitability for Drug Design. Preprints. https://doi.org/10.20944/preprints202109.0275.v1
Chicago/Turabian Style
Chitara, D. and Sanjeev B. S.. 2021 "Big Data Approach to Large Scale Molecular Dynamics Simulations: Necessity and Inevitability for Drug Design" Preprints. https://doi.org/10.20944/preprints202109.0275.v1
Abstract
Molecular Dynamics (MD) simulations model motion of molecules in atomistic detail and aid in drug design. While simulations on large systems may require several days to complete, analysis of terabytes of data generated in the process could also be time consuming. Recent studies captured exciting and dramatic drug-receptor interactions under cell-like complex conditions. Such advances make simulations of biomolecular interactions more realistic, insightful, and informative and have potential to make drug design more realistic. However, currently available resources and techniques do not provide, in reasonable time, a comprehensive understanding of events seen in simulations. We demonstrate that big data approach results in significant speedups, and provides rapid insights into simulations performed. Advancing this improvement, we propose a scalable, self-tuning, and responsive framework based on Cloud-infrastructure to accomplish the best possible MD studies with given priorities and within available resources.
Keywords
Cloud infrastructure; Spark; Molecular Dynamics simulations; Drug design.
Subject
Medicine and Pharmacology, Pharmacy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
