Quantification of emerging contaminants with liquid chromatography high resolution mass spectrometry and graph-based machine learning

Understanding the toxicity of real-world complex mixtures, and identification of the most toxic chemicals is an essential starting point for the intelligent design of water treatment solutions and chemical regulation.

To evaluate the risk possessed by water contaminants to humans and wildlife we need to know the structure, concentration (exposure), and toxic endpoint of the contaminants.

Liquid chromatography-electrospray mass spectrometry (LC/ESI/HRMS) in combination with machine learning has offered significant improvements in detecting, identifying, and estimating the toxic endpoint for hundreds and thousands of water contaminants simultaneously; however, the quantification is lagging.

The main reason is that analytical standards are required for the quantification of the detected contaminants.In this project, we will develop machine learning methods to quantify the contaminants detected with LC/ESI/HRMS even if analytical standards are not available.

Specifically, we will develop machine learning models to predict the response of the detected compounds in LC/ESI/HRMS from the structure of the compounds and use this for the quantification of these compounds.

This will allow us to evaluate the risk possessed by the detected compounds, without time-consuming synthesis of the analytical standards. Moreover, it will become possible to pinpoint the compound(s) with the greatest contribution to the mixture toxicity.