Machine Learning and Mass Spectrometry for Structural Elucidation of Novel Toxic Chemicals

Introduction

Nearly half a million known chemicals have been deemed relevant for exposure studies, and an even larger number of their transformation products are likely to co-occur in the environment. This mind-blowing number of possible chemical structures makes it impossible to in-silico generate all these structures, let alone synthesize and analytically confirm them, thereby limiting the discovery of novel chemicals.

Current Limitations

Today, the structural elucidation of chemicals detected with high-resolution mass spectrometry relies on databases and machine learning models trained on the known chemical space. Both are fundamentally ill-suited for discovering novel chemical structures. As a result, only a few percent of the toxic activity of the environmental samples is explained by the currently known and monitored chemicals.

Importance of Novel Chemical Space

It is crucial to access the novel chemical space to improve our understanding of the origin, fate, and impact of these chemicals.

Project Aim

The aim of LearningStructurE is to turn the discovery of novel chemical structures from serendipity to routine. As a steppingstone in this pursuit, I will combine the fundamental understanding of chromatography and high-resolution mass spectrometry with machine learning to pinpoint novel toxic chemical structures based on their empirical analytical information.

Methodology

To significantly advance the predictive power of machine learning models for empirical analytical information, I will take advantage of the candidate structures as a sample-specific training set for machine learning models. The improved predictive power will feed into in-silico structure generation, allowing elucidation of the structure directly from the empirical analytical information.

Expected Outcomes

LearningStructurE will pave the way for exploration of the unknown chemical space detected from environmental samples, and thereby improve our understanding of the emissions, chemical processes transforming the emitted chemicals, and close the gap in measured and explained toxicity.