Retention time prediction to facilitate molecularstructure identification with tandem mass spectrometry

Abstract

Comparing measured and predicted chromatographic retention time can improve molecular structure assignment in applications such as coupled liquid chromatography-tandem mass spectrometry. We assess a range of different machine learning methods to predict hydrophobicity, a molecular property that can be used as a proxy for retention time. The performance of the models is evaluated on the benchmark Martel and SAMPL7 datasets. We find that more powerful models perform better when predicting in-sample but not necessarily when generalizing to out-of-sample molecular families. We also find that ensemble methods can outperform individual models. Additionally, a multitask learning model shows promise for improving the generalization ability of graph neural networks for hydrophobicity prediction. Finally, we discuss how the ability of graph neural networks to generalize for molecular property prediction could be improved further.