Molecular structure elucidation by integrating different data mining strategies


The overall goal of this project is to develop and implement advanced data-driven programming tools, enabling a superior insight into ultra-high performance liquid chromatography coupled to high- resolution mass spectrometry (UHPLC-HRMS) data. While HPLC has been used as the first level of analyte separation since the 1960s, HRMS is a relatively new and powerful analytic technique used for discovery of molecular species based on their exact mass to charge ratio (m/z). The instrumentation applied is capable of separating mass fragments at the fourth or fifth decimal place. The additional information narrows down the possible chemical formulas of a molecule and thus allows an unprecedented unambiguous qualitative and quantitative assessment of the composition of various types of samples. Not surprisingly, HRMS has found applications across a broad spectrum of scientific fields.

Although we can routinely discern hundreds to thousands of molecular ‘features’ in complex samples such as blood, aerosols, soil, or biofuels, the complexity of the resulting data stream increases proportionally, producing millions of data points per second in multidimensional space. Thus post-processing and data reduction methods followed by data mining and innovative visualization techniques are required to yield meaningful information from HRMS. The project is about developing semi-automatic methods to confidently pinpoint each unknown molecular structure. It is a unique opportunity to expand the applicability of both HRMS and the Kendrick Mass Defect (KMD) approach beyond their current state-of-the-art applications, as well as beyond the capabilities of other analytic methods such as NMR and X-ray crystallography tools that typically require pure samples in relatively large amounts.

Starting Date

April 2019

PI / Partners

Catalytic Process Engineering Research Group (PSI)


  • Molecular clustering based on UHPLC-HRMS/MS data reflecting chemical “families” based on the presence of similar functional groups

  • Within-cluster prediction of functional groups and molecular structure for unknown compounds

  • Predictive modelling of molecular fragmentation patterns, retention time, and other features

Fig. 1: Fragmentation spectra for two dicarboxylic acids illustrating clear differences in fragment patterns and intensities despite similar structures.


  • Wu et al. (2021) Valence Photoionization and Energetics of Vanillin, a Sustainable Feedstock Candidate, The Journal of Physical Chemistry A, doi: 10.1021/acs.jpca.1c00876

  • Dührkop et al. (2020) Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra, Nature Biotechnology, doi: 10.1038/s41587-020-0740-8

  • Arturi et al. (2019) Molecular footprint of co-solvents in hydrothermal liquefaction (HTL) of Fallopia Japonica, Journal of Supercritical Fluids, doi: 10.1016/j.supflu.2018.08.010

  • Roach et al. (2011) Higher-Order Mass Defect Analysis for Mass Spectra of Complex Organic Mixtures, Analytical Chemistry, doi: 10.1021/ac200654j