路径采样在MD和QM/MM MD仿真的Python工具包。

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL
Lian Duan, Kowit Hengphasatporn* and Yasuteru Shigeta, 
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引用次数: 0

摘要

PaCS-Q是一个开源的Python工具包,它简化了QM/MM MD和MD模拟,使复杂的路径采样易于访问和用户友好。它与AMBER MD套件无缝集成,使用并行级联选择(PaCS)算法自动进行QM/MM MD模拟,无需预定义的反应坐标即可有效探索反应路径。PaCS-Q支持RMSD和基于距离的采样,这是研究共价反应和配体结合/解结合事件的理想选择。一个关键特性是它能够直接从代表性结构自动生成高斯和ORCA的QM输入文件,简化从MD到量子计算的过渡。通过内置的结构分析和能量分析工具,PaCS-Q最大限度地降低了设置的复杂性,并提高了可重复性。PaCS-Q易于通过pip安装,并与基于unix的系统兼容,为计算化学和药物发现的研究人员提供了实用、通用的解决方案,实现了快速、准确、省力的高级模拟。PaCS-Q Python工具包可在https://github.com/nyelidl/PaCS-Q/公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

PaCS-Q: Python Toolkits for Path Sampling in MD and QM/MM MD Simulation

PaCS-Q: Python Toolkits for Path Sampling in MD and QM/MM MD Simulation

PaCS-Q is an open-source Python toolkit that simplifies QM/MM MD and MD simulations, making complex pathway sampling accessible and user-friendly. Seamlessly integrated with the AMBER MD suite, it automates QM/MM MD simulations using the parallel cascade selection (PaCS) algorithm, enabling efficient exploration of reaction pathways without predefined reaction coordinates. PaCS-Q supports both RMSD- and distance-based sampling, which is ideal for studying covalent reactions and ligand binding/unbinding events. A key feature is its ability to automatically generate QM input files for Gaussian and ORCA directly from representative structures, streamlining the transition from MD to quantum calculations. With built-in tools for structure analysis and energy profiling, PaCS-Q minimizes setup complexity and enhances reproducibility. Easy to install via pip and compatible with Unix-based systems, PaCS-Q offers a practical, versatile solution for researchers in computational chemistry and drug discovery, enabling advanced simulations with speed, accuracy, and minimal effort. The PaCS-Q Python toolkit publicly available at https://github.com/nyelidl/PaCS-Q/.

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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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