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Finding potentially erroneous entries in METLIN SMRT.

Mikhail Khrisanfov¹, Dmitriy Matyushin², Andrey Samokhin¹

¹A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, Moscow, Russia; Chemistry Department, Lomonosov Moscow State University, Moscow, Russia.

Journal of Chromatography. A

|February 15, 2025

Summary

A new method effectively filters erroneous entries in the METLIN SMRT dataset for high-performance liquid chromatography (HPLC). This approach enhances data quality for machine learning models and experimental use.

Keywords:

HPLC High-performance liquid chromatography METLIN SMRT Machine learning Retention times

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Area of Science:

Analytical Chemistry
Computational Chemistry
Cheminformatics

Background:

The METLIN SMRT dataset is crucial for retention time prediction in high-performance liquid chromatography (HPLC).
Existing data filtering pipelines for METLIN SMRT are often inadequate, leading to potential inaccuracies.
A reliable method for identifying and removing erroneous entries is needed to improve dataset quality.

Purpose of the Study:

To adapt and apply a robust filtering method for potentially erroneous entries in the METLIN SMRT dataset.
To enhance the reliability of the METLIN SMRT dataset for both machine learning applications and experimental research.
To assess the efficacy of predictive models in identifying data anomalies within large-scale chromatography datasets.

Main Methods:

Repurposed an existing filtering approach for gas chromatography retention indexes to the METLIN SMRT dataset.
Employed five predictive models: Graph Neural Network (GNN), Convolutional Neural Network (CNN), Extended Connectivity Fingerprints (ECFP), Functional Connectivity Density (FCD), and CatBoost.
Utilized a 5-fold cross-validation strategy for retention time prediction and flagged entries with significant deviations (bottom 5%) using a "yellow card" system.

Main Results:

Approximately 1500 entries (2% of the dataset) received at least one "yellow card" across the five models.
An estimated 1200 entries were identified as strongly suspected erroneous data points.
Approximately 300 entries were flagged as likely inaccurate predictions, distinct from erroneous entries.

Conclusions:

The developed filtering approach is viable for improving the quality of the METLIN SMRT dataset.
This method holds significant potential for enhancing other large-scale chromatography-related databases.
Improved data quality benefits both the training of machine learning models and direct experimental utilization.