了解短期和长期分子间相互作用在新型计算药物发现中的作用。

IF 4.9 2区 医学 Q1 PHARMACOLOGY & PHARMACY
Samuel S Cho, A Salam
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引用次数: 0

摘要

了解官能团、配体、分子片段以及整个分子之间的相互作用,对现代药物发现至关重要。这一努力的关键是基本粒子间力的理论发展,以及它们在许多软件包中的实现,这些软件包允许在不同的理论水平上计算相互作用能量,从一个极端的完全经典到另一个极端的完全量子力学。包括的领域:在这篇综述中,作者考虑了分子间势能函数的概念和它的短期和长期区域的分离。然后总结了通过扩展源多极矩的电荷分布来计算静电、感应和色散能量转移的微扰理论。其次,作者概述了典型分子力场的构建及其参数化;他们还讨论了分子动力学(MD)模拟的基本背景,它们在几个知名软件包中的实现以及它们在现代计算药物发现中的部署,包括最近与人工智能和机器学习技术的合作。SSC引用的论文是在2025年1 - 7月期间使用PubMed和谷歌Scholar进行的文献检索以及作者个人文献库存的结果。专家意见:虽然分子间作用力的量子力学理论是众所周知的,但它们对不断增长的各种日益复杂的系统的精确可靠的计算反映了执行此类模拟的计算硬件的进步。再加上新兴的机器学习技术,这使得快速有效的计算机辅助发现潜在的新候选药物成为可能,在这一过程中,学术界和工业界的研究和开发都发生了革命性的变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding the role of short- and long-range intermolecular interactions in novel computational drug discovery.

Introduction: Understanding the interactions between functional groups, ligands, molecular fragments, and whole molecules is critical in modern drug discovery. Key to this endeavor is the theoretical development of the fundamental inter-particle forces at play and their implementation in numerous software packages that allow the calculation of interaction energies at varying levels of theory ranging from the entirely classical at one extreme to the fully quantum mechanical at the other.

Areas covered: In this review, the authors consider the concept of an intermolecular potential energy function and its separation into short- and long-range regions. This is followed by a summary of the perturbation theory calculation of the electrostatic, induction, and dispersion energy shifts by expanding the charge distribution in terms of source multipole moments. Next, the authors outline the construction of a typical molecular force field and its parameterization; they also discuss the fundamental background of molecular dynamics (MD) simulations, their implementation in several well-known software packages and their deployment in modern computational drug discovery, including recent work with Artificial Intelligence and Machine Learning techniques. Papers cited by SSC were the result of a literature search conducted using PubMed and Google Scholar during Jan-July 2025 as well as from the authors' personal literature stock.

Expert opinion: While the underlying quantum mechanical theory of intermolecular forces is well-known, their accurate and reliable calculation for an ever-growing variety of increasingly complex systems mirrors the advances in computational hardware on which such simulations are performed. Coupled with emerging machine learning techniques, this allows for the rapid and efficient computer-aided discovery of potential new drug candidates, in the process revolutionizing research and development in both academia and industry.

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来源期刊
CiteScore
10.20
自引率
1.60%
发文量
78
审稿时长
6-12 weeks
期刊介绍: Expert Opinion on Drug Discovery (ISSN 1746-0441 [print], 1746-045X [electronic]) is a MEDLINE-indexed, peer-reviewed, international journal publishing review articles on novel technologies involved in the drug discovery process, leading to new leads and reduced attrition rates. Each article is structured to incorporate the author’s own expert opinion on the scope for future development. The Editors welcome: Reviews covering chemoinformatics; bioinformatics; assay development; novel screening technologies; in vitro/in vivo models; structure-based drug design; systems biology Drug Case Histories examining the steps involved in the preclinical and clinical development of a particular drug The audience consists of scientists and managers in the healthcare and pharmaceutical industry, academic pharmaceutical scientists and other closely related professionals looking to enhance the success of their drug candidates through optimisation at the preclinical level.
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