Highly accurate and large-scale collision cross sections prediction with graph neural networks

作者：Renfeng Guo, Youjia Zhang, Yuxuan Liao, Qiong Yang, Ting Xie, Xiaqiong Fan, Zhonglong Lin, Yi Chen, Hongmei Lü, Zhimin Zhang · 发表于：Communications Chemistry · 年份：2023 · DOI：10.1038/s42004-023-00939-w · 被引用次数：33 · 研究领域：Mass Spectrometry Techniques and Applications、Metabolomics and Mass Spectrometry Studies、Analytical Chemistry and Chromatography

The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS . Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures.