3DMolMS:从3D分子构造中预测双重质谱
Yuhui Hong1, Sujun Li1,2, Christopher J Welch3
1Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN 47408, United States.
Bioinformatics (Oxford, England)
|May 30, 2023
概括
我们开发了用于质谱预测的3D分子网络 (3DMolMS),这是一个深度学习模型,可以从3D结构中预测复合MS/MS光谱. 这有助于自动化化合物识别,特别是对于新型分子.
科学领域:
- 计算化学是一种计算化学.
- 频谱学是一种光谱学.
- 人工智能的人工智能是人工智能.
背景情况:
- 双重质谱 (MS/MS) 对于高通量化学化合物表征至关重要.
- 从MS/MS光谱中自动识别化合物是有限的,特别是对于新型化合物.
- 现有的in silico光谱预测方法往往忽略了关键的3D结构信息.
研究的目的:
- 介绍用于质谱预测的3D分子网络 (3DMolMS),一个深度神经网络.
- 预测化合物的MS/MS光谱,利用它们的3D形状.
- 改进自动化化合物识别的计算方法.
主要方法:
- 开发了一个名为3DMolMS的深度神经网络模型.
- 模型的输入包括3D分子构造.
- 模型预测了化合物的MS/MS光谱.
主要成果:
- 3DMolMS与实验MS/MS光谱相比,实现了0.691 (正离子模式) 和0.478 (负离子模式) 的平均等号相似性.
- 该模型展示了在不同的实验室和仪器中具有微小微调的概括能力.
- 学习的分子表示增强了液体染色学化时间和离子流动性碰撞横截面的预测.
结论:
- 3DMolMS有效地从3D形状预测MS/MS光谱,解决当前方法的局限性.
- 该模型的适应性及其学习表示的实用性为化合物识别提供了显著的潜力.
- 这项工作为推进化学分析和发现提供了有价值的工具和方法.
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