Chemometrics is the science of extracting information from chemical systems by data-driven means. Chemometrics is inherently interdisciplinary, using methods frequently employed in core data-analytic disciplines such as multivariate statistics, applied mathematics, and computer science, in order to address problems in chemistry, biochemistry, medicine, biology and chemical engineering.
In our research, we develop and apply advanced chemometrics and machine learning methods to face real problems in chemistry, toxicology, food authenticity, metabolomics, pharmacology and environmental sciences.
Our studies are especially focused on developing chemometrics-assisted workflow for non-target and suspect screening of large MS-ready chemical database.
We are also engaged with the development of Quantitative Structure – Activity Relationships (QSARs) to predict toxicity and any physicochemical properties of novel chemicals for prioritization purpose.