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快速和自动化 Ab Initio 代谢物碰撞截面预测从SMILES输入

Susanta Das¹, Laleh Dinpazhoh¹, Kiyoto Aramis Tanemura¹

¹Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States.

Journal of chemical information and modeling

|August 7, 2023

概括

我们开发了一个自动化工作流来预测小型有机分子的碰撞横截面 (CCS) 值. 这种方法增强了代谢学数据分析,并提供高准确度的可重现结果,即使在加速时也是如此.

科学领域:

计算化学是一种计算化学.
化学信息学化学信息学
分析化学是一种分析化学.

背景情况:

碰撞截面 (CCS) 对于注释非目标代谢学数据至关重要.
准确的CCS预测有助于识别小型有机分子.
当前的预测方法可以是计算密集的.

研究的目的:

开发一种自动化的高性能计算 (HPC) 工作流程,用于预测博尔兹曼平均 CCS 值.
评估CCS预测工作流程的准确性和可重复性.
为了证明产生的气相合并用于预测其他分子性质的实用性.

主要方法:

一个*ab initio*的工作流程,整合了分子力学,深度学习潜力,形态聚类和量子力学 (QM).
针对HPC环境的自动结构生成和改进步骤.
通过用单点能量计算取代QM几何精细化来简化CCS计算.

主要成果:

工作流接受SMILES字符串并输出准确的,博尔茨曼平均的CCS值.
通过用单点能量取代QM精炼来加快计算速度,只产生了轻微的精度下降.
在CCS预测工作流中,尽管存在随机元素,但L-卡诺辛的可重现性很高.

结论:

自动化的CCS预测工作流程为小型有机分子提供了强大的和可重复的方法.
工作流可以在最小的准确性损失下加快,从而提高计算效率.
生成的气相组合具有多功能性,可用于预测其他属性,例如NMR化学转移.

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