Quantum-machine-assisted Drug Discovery: Survey and Perspective
<|reference_start|>Quantum-machine-assisted Drug Discovery: Survey and Perspective: 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.<|reference_end|>
@article{zhou2024quantum-machine-assisted,
    title={Quantum-machine-assisted Drug Discovery: Survey and Perspective},
    author={Yidong Zhou, Jintai Chen, Jinglei Cheng, Gopal Karemore, Marinka
  Zitnik, Frederic T. Chong, Junyu Liu, Tianfan Fu, Zhiding Liang},
    journal={arXiv preprint arXiv:2408.13479},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13479},
    primaryClass={quant-ph cs.LG q-bio.BM}
}