Cheminformatics and Computational Approaches for Identifying and Managing Unknown Chemicals in the Environment

In most societies, using chemical products has become a part of daily life. Worldwide, over 350,000 chemicals have been registered for use in e.g., daily household consumption, industrial processes, agriculture, etc. However, despite the benefits chemicals may bring to society, their usage, production, and disposal, which leads to their eventual release into the environment has multiple implications. Anthropogenic chemicals have been detected in myriad ecosystems all over the planet, as well as in the tissues of wildlife and humans. The potential consequences of such chemical pollution are not fully understood, but links to the onset of human disease and threats to biodiversity have been attributed to the presence of chemicals in our environment. Mitigating the potential negative effects of chemicals typically involves regulatory steps and multiple stakeholders. One key aspect thereof is environmental monitoring, which consists of environmental sampling, measurement, data analysis, and reporting. In recent years, advancements in Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS), open chemical databases, and software have enabled researchers to identify known (e.g., pesticides) as well as unknown environmental chemicals, commonly referred to as suspect or non-target compounds. However, identifying unknown chemicals, particularly non-targets, remains extremely challenging because of the lack of a priori knowledge on the analytes - all that is available are their mass spectrometry signals. In fact, the number of unknown features in a typical mass spectrum of an environmental sample is in the range of thousands to tens of thousands, and therefore requires feature prioritisation before identification within a suitable workflow. In this dissertation work, collaborations with two regulatory authorities responsible for environmental monitoring sought to identify relevant unknown compounds in the environment, specifically by developing computational workflows for unknown identification in LC-HRMS data. ...