Automatic molecular fragmentation by evolutionary optimisation

IF 7.1 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Fiona C. Y. Yu, Jorge L. Gálvez Vallejo, Giuseppe M. J. Barca
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引用次数: 0

Abstract

Molecular fragmentation is an effective suite of approaches to reduce the formal computational complexity of quantum chemistry calculations while enhancing their algorithmic parallelisability. However, the practical applicability of fragmentation techniques remains hindered by a dearth of automation and effective metrics to assess the quality of a fragmentation scheme. In this article, we present the Quick Fragmentation via Automated Genetic Search (QFRAGS), a novel automated fragmentation algorithm that uses a genetic optimisation procedure to generate molecular fragments that yield low energy errors when adopted in Many Body Expansions (MBEs). Benchmark testing of QFRAGS on protein systems with less than 500 atoms, using two-body (MBE2) and three-body (MBE3) MBE calculations at the HF/6-31G* level, reveals mean absolute energy errors (MAEE) of 20.6 and 2.2 kJ \(\hbox {mol}^{-1}\), respectively. For larger protein systems exceeding 500 atoms, MAEEs are 181.5 kJ \(\hbox {mol}^{-1}\) for MBE2 and 24.3 kJ \(\hbox {mol}^{-1}\) for MBE3. Furthermore, when compared to three manual fragmentation schemes on a 40-protein dataset, using both MBE and Fragment Molecular Orbital techniques, QFRAGS achieves comparable or often lower MAEEs. When applied to a 10-lipoglycan/glycolipid dataset, MAEs of 7.9 and 0.3 kJ \(\hbox {mol}^{-1}\) were observed at the MBE2 and MBE3 levels, respectively.

Scientific Contribution This Article presents the Quick Fragmentation via Automated Genetic Search (QFRAGS), an innovative molecular fragmentation algorithm that significantly improves upon existing molecular fragmentation approaches by specifically addressing their lack of automation and effective fragmentation quality metrics. With an evolutionary optimisation strategy, QFRAGS actively pursues high quality fragments, generating fragmentation schemes that exhibit minimal energy errors on systems with hundreds to thousands of atoms. The advent of QFRAGS represents a significant advancement in molecular fragmentation, greatly improving the accessibility and computational feasibility of accurate quantum chemistry calculations.

通过进化优化实现自动分子破碎
分子破碎是一套有效的方法,可降低量子化学计算的形式计算复杂度,同时提高算法的并行性。然而,由于缺乏自动化和有效的指标来评估分片方案的质量,分片技术的实际应用仍然受到阻碍。在这篇文章中,我们介绍了 "通过自动遗传搜索进行快速片段化"(QFRAGS),这是一种新型的自动片段化算法,它使用遗传优化程序生成分子片段,在多体扩展(MBE)中使用时产生低能量误差。通过在 HF/6-31G* 水平上使用二体(MBE2)和三体(MBE3)MBE 计算,对少于 500 个原子的蛋白质系统进行了 QFRAGS 基准测试,结果显示平均绝对能量误差(MAEE)分别为 20.6 和 2.2 kJ $\hbox {mol}^{-1}$。对于超过 500 个原子的大型蛋白质系统,MBE2 的平均绝对能量误差为 181.5 kJ $\hbox {mol}^{-1}$ 和 MBE3 的平均绝对能量误差为 24.3 kJ $\hbox {mol}^{-1}$。此外,在使用 MBE 和片段分子轨道技术对 40 个蛋白质数据集进行人工片段分析时,QFRAGS 与三种人工片段分析方案进行了比较,QFRAGS 可获得相当或更低的 MAEE。当应用于 10 个脂聚糖/糖脂数据集时,在 MBE2 和 MBE3 水平上观察到的 MAE 分别为 7.9 和 0.3 kJ $\hbox {mol}^{-1}$ 。科学贡献 本文介绍了 "通过自动遗传搜索进行快速破碎"(QFRAGS),这是一种创新的分子破碎算法,通过专门解决现有分子破碎方法缺乏自动化和有效破碎质量指标的问题,大大改进了现有的分子破碎方法。QFRAGS 采用进化优化策略,积极追求高质量的片段,生成的片段方案在拥有数百到数千个原子的系统中表现出最小的能量误差。QFRAGS 的出现代表了分子破碎领域的重大进步,大大提高了精确量子化学计算的可及性和计算可行性。
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来源期刊
Journal of Cheminformatics
Journal of Cheminformatics CHEMISTRY, MULTIDISCIPLINARY-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
14.10
自引率
7.00%
发文量
82
审稿时长
3 months
期刊介绍: Journal of Cheminformatics is an open access journal publishing original peer-reviewed research in all aspects of cheminformatics and molecular modelling. Coverage includes, but is not limited to: chemical information systems, software and databases, and molecular modelling, chemical structure representations and their use in structure, substructure, and similarity searching of chemical substance and chemical reaction databases, computer and molecular graphics, computer-aided molecular design, expert systems, QSAR, and data mining techniques.
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