Zhou, Y.; Karemore, G.; Li, W.; Liang, Z.; Zitnik, M.; Chong, F.; Liu, J.; Fu, T. Quantum-Machine-Assisted Drug Discovery: Survey and Perspective. Preprints2024, 2024081950. https://doi.org/10.20944/preprints202408.1950.v1
APA Style
Zhou, Y., Karemore, G., Li, W., Liang, Z., Zitnik, M., Chong, F., Liu, J., & Fu, T. (2024). Quantum-Machine-Assisted Drug Discovery: Survey and Perspective. Preprints. https://doi.org/10.20944/preprints202408.1950.v1
Chicago/Turabian Style
Zhou, Y., Junyu Liu and Tianfan Fu. 2024 "Quantum-Machine-Assisted Drug Discovery: Survey and Perspective" Preprints. https://doi.org/10.20944/preprints202408.1950.v1
Abstract
Drug discovery and development is a highly complex and costly endeavor, typically requiring over a decade and substantial financial investment to bring a new drug to market. Traditional computer-aided drug design (CADD) has made significant progress in accelerating this process, but the development of quantum computing offers potential due to its unique capabilities. This paper discusses the integration of quantum computing into drug discovery and development, focusing on how quantum technologies might accelerate and enhance various stages of the drug development cycle. Specifically, we explore the application of quantum computing in addressing challenges related to drug discovery, such as molecular simulation and the prediction of drug-target interactions, as well as the optimization of clinical trial outcomes. By leveraging the inherent capabilities of quantum computing, we might be able to reduce the time and cost associated with bringing new drugs to market, ultimately benefiting public health.
Keywords
Quantum Computing; Drug Discovery; Clinical Trails; Quantum Algorithms
Subject
Physical Sciences, Quantum Science and Technology
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.
