Yang Qiong, Ji Hongchao, Lu Hongmei, Zhang Zhimin
College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
Anal Chem. 2021 Feb 2;93(4):2200-2206. doi: 10.1021/acs.analchem.0c04071. Epub 2021 Jan 7.
The predicted liquid chromatographic retention times (RTs) of small molecules are not accurate enough for wide adoption in structural identification. In this study, we used the graph neural network to predict the retention time (GNN-RT) from structures of small molecules directly without the requirement of molecular descriptors. The predicted accuracy of GNN-RT was compared with random forests (RFs), Bayesian ridge regression, convolutional neural network (CNN), and a deep-learning regression model (DLM) on a METLIN small molecule retention time (SMRT) dataset. GNN-RT achieved the highest predicting accuracy with a mean relative error of 4.9% and a median relative error of 3.2%. Furthermore, the SMRT-trained GNN-RT model can be transferred to the same type of chromatographic systems easily. The predicted RT is valuable for structural identification in complementary to tandem mass spectra and can be used to assist in the identification of compounds. The results indicate that GNN-RT is a promising method to predict the RT for liquid chromatography and improve the accuracy of structural identification for small molecules.
小分子的预测液相色谱保留时间（RTs）不够准确，无法广泛应用于结构鉴定。在本研究中，我们使用图神经网络直接从小分子结构预测保留时间（GNN-RT），而无需分子描述符。在METLIN小分子保留时间（SMRT）数据集上，将GNN-RT的预测准确性与随机森林（RFs）、贝叶斯岭回归、卷积神经网络（CNN）和深度学习回归模型（DLM）进行了比较。GNN-RT实现了最高的预测准确性，平均相对误差为4.9%，中位数相对误差为3.2%。此外，经SMRT训练的GNN-RT模型可以轻松转移到相同类型的色谱系统中。预测的RT对于与串联质谱互补的结构鉴定很有价值，可用于辅助化合物鉴定。结果表明，GNN-RT是一种很有前景的预测液相色谱RT和提高小分子结构鉴定准确性的方法。
