Recent advances in neural networks (NNs) and their implementation into the compound property prediction have led to significant achievements in QSRR . Feed-forward NNs, convolutional NNs, attentive fingerprints, and graph NNs (GNNs; in particular, Chemprop ) achieve mean absolute errors close to mean chromatographic peak width and coefficients of determination (R 2 ) above 0.9. Among these, GNNs shows one of the best model performance and ease of use due to the unambiguous selection of the descriptors. Unlike other research areas, the availability of relatively large open access data, such as METLIN small molecule retention time dataset (METLIN SMRT) allows easy comparison and benchmarking of different QSRR models. However, it has been reported that the METLIN SMRT RT values with different spectra acquisition experiments exhibits changes in molecular RT over time, even if the instrument and LC-MS method remain the same, resulting in general model errors. The aim of this work was to develop a robust RT prediction model for our in-house routine LC-MS analysis and implement it into the existing LC-MS automated assessment functional of the company's production workflow (Figure ). As part of this task, we address the following points: