Finding potentially erroneous entries in METLIN SMRT

METLIN SMRT is a widely-used dataset of retention times for high-performance liquid chromatography (HPLC). Besides direct application it is used for training models aimed at predicting retention times in HPLC. Although there are quite a number of articles featuring METLIN SMRT, the pipelines used for filtering from errors are either simplistic or nonexistent. Therefore, a reliable method for filtering potentially erroneous entries is still required. An approach to filter potentially erroneous entries, suggested in our earlier work for a database of gas chromatography retention indexes, was repurposed for METLIN SMRT using five predictive models (GNN, CNN, FCFP, FCD, and CatBoost). The retention times were predicted for the whole dataset using a 5-fold cross-validation strategy. Entries with retention times differing significantly from the predictions obtained from a given model (bottom 5%) were flagged with a “yellow card”. This procedure was repeated for each model, leading to a group containing about 1500 entries (or 2% of the dataset) with 5 “yellow cards”. According to our estimate (derived from analyzing trends and distributions for groups with varying numbers of “yellow cards”) about 1200 entries were strongly suspected to be erroneous, while 300 were likely predicted inaccurately. This work demonstrates the viability of the approach and its potential to improve the quality of other large-scale chromatography-related databases for both machine learning and experimental use.