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

Projektbeschreibung

Maschinelles Lernen hilft bei Entdeckung neuer toxischer chemischer Strukturen

Expositionsstudien sind ein wichtiger Bestandteil der chemischen Forschung. Ungefähr eine halbe Million Chemikalien wurden als relevant für solche Studien gekennzeichnet, wobei eine beträchtliche Anzahl ihrer Umwandlungsprodukte in der Umwelt koexistiert. Bedauerlicherweise können trotz dieses umfangreichen Spektrums nur wenige dieser chemischen Strukturen in silico erzeugt, analysiert und validiert werden. Aktuelle Datenbanken und Modelle des maschinellen Lernens stützen sich auf diese vorhandenen chemischen Strukturen. Im Rahmen des ERC-finanzierten Projekts LearningStructurE soll diese Landschaft revolutioniert werden, indem es maschinelles Lernen mit neuen Technologien kombiniert, um neue toxische chemische Strukturen zu bestimmen. Ziel dieser Initiative ist es, den Prozess der Entdeckung neuer chemischer Strukturen zu rationalisieren und seine Zugänglichkeit, Frequenz und Effizienz zu erhöhen.

Ziel

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 synthesise and analytically confirm them, thereby limiting the discovery of novel chemicals. 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. It is crucial to access the novel chemical space to improve our understanding of the origin, fate, and impact of these chemicals.

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. 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 to elucidate the structure directly from the empirical analytical information.

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.