Yu Fiona C Y, Vallejo Jorge L Gálvez, Barca Giuseppe M J
School of Computing, Australian National University, Canberra, 2601, ACT, Australia.
School of Computing and Information Technology, The University of Melbourne, Melbourne, 3052, VIC, Australia.
J Cheminform. 2024 Aug 19;16(1):102. doi: 10.1186/s13321-024-00896-z.
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 , respectively. For larger protein systems exceeding 500 atoms, MAEEs are 181.5 kJ for MBE2 and 24.3 kJ 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 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.
分子碎片化是一套有效的方法，可降低量子化学计算的形式计算复杂度，同时提高其算法并行性。然而，碎片化技术的实际适用性仍然受到自动化程度不足以及缺乏评估碎片化方案质量的有效指标的阻碍。在本文中，我们提出了通过自动遗传搜索的快速碎片化方法（QFRAGS），这是一种新颖的自动碎片化算法，它使用遗传优化程序来生成分子片段，这些片段在多体展开（MBE）中使用时产生的能量误差较小。在HF/6-31G*水平下，使用两体（MBE2）和三体（MBE3）MBE计算对原子数少于500的蛋白质系统进行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 。对于超过500个原子的较大蛋白质系统，MBE2的MAEE为181.5 kJ ，MBE3的MAEE为24.3 kJ 。此外，与40个蛋白质数据集上的三种手动碎片化方案相比，使用MBE和片段分子轨道技术，QFRAGS实现了相当或更低的MAEE。当应用于10个脂多糖/糖脂数据集时，在MBE2和MBE3水平下观察到的平均绝对误差（MAE）分别为7.9和0.3 kJ 。科学贡献 本文提出了通过自动遗传搜索的快速碎片化方法（QFRAGS），这是一种创新的分子碎片化算法，通过专门解决现有分子碎片化方法缺乏自动化和有效碎片化质量指标的问题，对现有方法进行了显著改进。通过进化优化策略，QFRAGS积极追求高质量片段，生成在具有数百到数千个原子的系统上能量误差最小的碎片化方案。QFRAGS的出现代表了分子碎片化的重大进展，大大提高了精确量子化学计算的可及性和计算可行性。
