Machine learning in chemistry and beyond" (ChE-651) seminar by Prof. Emma Schymanski: "Environmental Cheminformatics - Searching for Meaning Amongst Millions of Chemicals"

Exposomics researchers need to identify relevant chemicals covering all potential exposures over entire lifetimes. With over 100 million chemicals in the largest chemical databases, coupled with broadly acknowledged knowledge gaps, researchers are faced with too much yet not enough information at the same time. Improvements in analytical technologies and computational mass spectrometry workflows coupled with the rapid growth in databases and increasing demand for high throughput “big data” services from the research community present significant challenges for both data hosts and workflow developers. This talk will describe FAIR and Open Science developments in the Environmental Cheminformatics group, including the NORMAN Suspect List Exchange (NORMAN-SLE), MassBank, MetFrag, PubChemLite for Exposomics, the PubChem PFAS Tree, patRoon, ShinyTPs and the Chemical Stripes, and will show how these are applied in our active research projects to tackle challenges in non-target exposomics studies. Finally, this talk will touch on some of our latest work on patent data and critically assessing the implications of class definitions using the examples of per- and polyfluoroalkyl substances (PFAS) and persistent, mobile, toxic (PMT) compounds.