Evotec (UK) Ltd., Oxfordshire, UK.
Digital Futures Institute, University of Suffolk, Ipswich, UK.
Methods Mol Biol. 2024;2716:153-179. doi: 10.1007/978-1-0716-3449-3_7.
Novel medication development is a time-consuming and expensive multistage procedure. Recent technology developments have lowered timeframes, complexity, and cost dramatically. Current research projects are driven by AI and machine learning computational models. This chapter will introduce quantum computing (QC) to drug development issues and provide an in-depth discussion of how quantum computing may be used to solve various drug discovery problems. We will first discuss the fundamentals of QC, a review of known Hamiltonians, how to apply Hamiltonians to drug discovery challenges, and what the noisy intermediate-scale quantum (NISQ) era methods and their limitations are.We will further discuss how these NISQ era techniques can aid with specific drug discovery challenges, including protein folding, molecular docking, AI-/ML-based optimization, and novel modalities for small molecules and RNA secondary structures. Consequently, we will discuss the latest QC landscape's opportunities and challenges.
新药开发是一个耗时耗钱的多阶段过程。最近的技术发展大大缩短了时间、降低了复杂性和成本。当前的研究项目由人工智能和机器学习计算模型驱动。本章将介绍量子计算(QC)在药物开发问题中的应用,并深入讨论量子计算如何用于解决各种药物发现问题。我们将首先讨论 QC 的基本原理、已知哈密顿量的综述、如何将哈密顿量应用于药物发现挑战,以及嘈杂的中等规模量子(NISQ)时代的方法及其局限性。我们将进一步讨论这些 NISQ 时代的技术如何帮助解决特定的药物发现挑战,包括蛋白质折叠、分子对接、基于人工智能/机器学习的优化,以及小分子和 RNA 二级结构的新模态。因此,我们将讨论最新的 QC 景观的机遇和挑战。
Methods Mol Biol. 2024
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