Machine learning for assessing toxicity of chemicals identified with mass spectrometry
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Real-world samples can contain hundreds to thousands of chemicals, with endocrinedisrupting
chemicals (EDCs) posing a severe threat to human health. Unfortunately,
reliable and rapid methods for detecting these compounds from complex mixtures are
lacking. One of the potential solutions could be to leverage the capabilities of non-target
liquid chromatography high-resolution mass spectrometry (LC/HRMS) combined with
machine learning methods. This study aimed to investigate whether the biochemical
activity of compounds can be estimated based on chemical fingerprints calculated from
HRMS spectra and thereby flag the compounds that require further analysis due to the
potential risk they pose to human health. For that, several classification models based on a
variety of machine learning algorithms were trained, and their accuracy was evaluated
using chemical fingerprints derived from experimental mass spectra. As a result, it was
found that the proposed methodology has great potential in the field of in silico
toxicology.
Description
Keywords
High-resolution mass spectrometry, molecular fingerprints, endocrine disruptors, Tox21, multi-task learning