Extracting Structural Information from Physicochemical Property Measurements Using Machine LearningA New Approach for Structure Elucidation in Non-targeted Analysis

Non-targeted analysis (NTA) has made critical contributions
in
the fields of environmental chemistry and environmental health. One
critical bottleneck is the lack of available analytical standards
for most chemicals in the environment. Our study aims to explore a
novel approach that integrates measurements of equilibrium partition
ratios between organic solvents and water (KSW) to predictions of molecular structures. These properties
can be used as a fingerprint, which with the help of a machine learning
algorithm can be converted into a series of functional groups (RDKit
fragments), which can be used to search chemical databases. We conducted
partitioning experiments using a chemical mixture containing 185 chemicals
in 10 different organic solvents and water. Both a liquid chromatography
quadrupole time-of-flight mass spectrometer (LC-QTOF MS) and a LC-Orbitrap
MS were used to assess the feasibility of the experimental method
and the accuracy of the algorithm at predicting the correct functional
groups. The two methods showed differences in log KSW with the QTOF method showing a mean absolute error
(MAE) of 0.22 and the Orbitrap method 0.33. The differences also culminated
into errors in the predictions of RDKit fragments with the MAE for
the QTOF method being 0.23 and for the Orbitrap method being 0.31.
Our approach presents a new angle in structure elucidation for NTA
and showed promise in assisting with compound identification.