MolGC:用于计算分子键长平均绝对误差的分子几何比较器算法。

IF 3.9 2区 化学 Q2 CHEMISTRY, APPLIED
Javier Camarillo-Cisneros, Graciela Ramirez-Alonso, Carlos Arzate-Quintana, Hugo Varela-Rodríguez, Abimael Guzman-Pando
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引用次数: 0

摘要

密度泛函理论(DFT)被广泛应用于理论化学和计算化学,以研究量子化学、材料物理、催化、生物化学和表面科学等不同领域的分子和晶体特性。尽管用于优化几何结构的 DFT 硬件和软件不断进步,但要在分子结构与实验对应物的比较中达成共识仍是一项挑战。由于缺乏自动键长比较工具,导致人工操作过程耗费大量人力且容易出错,从而加剧了这一困难。为了应对这些挑战,我们提出了分子几何比较算法 MolGC,它可以自动比较不同理论水平的优化几何图形。MolGC 通过整合来自不同 DFT 软件的数据,计算键长的平均绝对误差 (MAE)。它提供交互式和可定制的几何图形可视化,使用户能够探索不同的视图以加强分析。此外,它还能保存 MAE 计算结果以作进一步分析,并提供全面的结果统计摘要。MolGC 能有效解决复杂的图形标记难题,确保对不同化学结构中的化学键进行准确识别和分类。它在抗生素数据集上的键标签分配正确率平均达到 98.91%,显示了它在比较不同复杂程度和大小的几何结构中分子键长度方面的有效性。运行 MolGC 的可执行文件和软件资源可从 https://github.com/AbimaelGP/MolGC/tree/main 下载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules

MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules

Density Functional Theory (DFT) is extensively used in theoretical and computational chemistry to study molecular and crystal properties across diverse fields, including quantum chemistry, materials physics, catalysis, biochemistry, and surface science. Despite advances in DFT hardware and software for optimized geometries, achieving consensus in molecular structure comparisons with experimental counterparts remains a challenge. This difficulty is exacerbated by the lack of automated bond length comparison tools, resulting in labor-intensive and error-prone manual processes. To address these challenges, we propose MolGC, a Molecular Geometry Comparator algorithm that automates the comparison of optimized geometries from different theoretical levels. MolGC calculates the mean absolute error (MAE) of bond lengths by integrating data from various DFT software. It provides interactive and customizable visualization of geometries, enabling users to explore different views for enhanced analysis. In addition, it saves MAE computations for further analysis and offers a comprehensive statistical summary of the results. MolGC effectively addresses complex graph labeling challenges, ensuring accurate identification and categorization of bonds in diverse chemical structures. It achieves a 98.91% average rate in correct bond label assignments on an antibiotics dataset, showcasing its effectiveness for comparing molecular bond lengths across geometries of varying complexity and size. The executable file and software resources for running MolGC can be downloaded from https://github.com/AbimaelGP/MolGC/tree/main.

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来源期刊
Molecular Diversity
Molecular Diversity 化学-化学综合
CiteScore
7.30
自引率
7.90%
发文量
219
审稿时长
2.7 months
期刊介绍: Molecular Diversity is a new publication forum for the rapid publication of refereed papers dedicated to describing the development, application and theory of molecular diversity and combinatorial chemistry in basic and applied research and drug discovery. The journal publishes both short and full papers, perspectives, news and reviews dealing with all aspects of the generation of molecular diversity, application of diversity for screening against alternative targets of all types (biological, biophysical, technological), analysis of results obtained and their application in various scientific disciplines/approaches including: combinatorial chemistry and parallel synthesis; small molecule libraries; microwave synthesis; flow synthesis; fluorous synthesis; diversity oriented synthesis (DOS); nanoreactors; click chemistry; multiplex technologies; fragment- and ligand-based design; structure/function/SAR; computational chemistry and molecular design; chemoinformatics; screening techniques and screening interfaces; analytical and purification methods; robotics, automation and miniaturization; targeted libraries; display libraries; peptides and peptoids; proteins; oligonucleotides; carbohydrates; natural diversity; new methods of library formulation and deconvolution; directed evolution, origin of life and recombination; search techniques, landscapes, random chemistry and more;
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