Machine learning for assessing toxicity of chemicals identified with mass spectrometry

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.