The protonated nucleobases (C + H) + , (T + H) + , (U + H) + , (A + H) + , and (G + H) + are investigated in a combined experimental and computational study using differential mobility spectrometry (DMS), mass spectrometry, and electronic structure calculations. DMS is used to isolate individual tautomeric forms for each protonated nucleobase prior to characterization with HDX or CID. The population distributions of each protonated nucleobase formed by electrospray ionization (ESI) are dominated by a single tautomeric form, as is predicted by our calculations. However, all nucleobases present additional tautomers upon ESI, with these minor contributions to the ensemble populations attributed to additional higher energy metastable species. In addition to the tautomerderived species, additional ion signals in the DMS data are attributed to larger nucleobasecontaining clusters, which fragment post-DMS to yield bare ion and fragment ion signals that are consistent with those expected for the bare protonated nucleobases. Contributions from larger clustered species are instead distinguished by monitoring DMS ion signal as declustering potential voltages are ramped.

The fast and accurate determination of molecular properties is highly desirable for many facets of chemical research, particularly in drug discovery where pre-clinical assays play an important role in paring down large sets of drug candidates. Here, we present the use of supervised machine learning to treat differential mobility spectrometry – mass spectrometry data for ten topological classes of drug candidates. We demonstrate that the gas-phase clustering behavior probed in our experiments can be used to predict the candidates’ condensed phase molecular properties, such as cell permeability, solubility, polar surface area, and water/octanol distribution coefficient. All of these measurements are performed in minutes and require mere nanograms of each drug examined. Moreover, by tuning gas temperature within the differential mobility spectrometer, one can fine tune the extent of ion-solvent clustering to separate subtly different molecular geometries and to discriminate molecules of very similar physicochemical properties.

A transformation product of trimethoprim, a contaminant of emerging concern in the environment, is generated using an electro-assisted Fenton reaction and analyzed using differential mobility spectrometry (DMS) in combination with MS/MS techniques and quantum chemical calculations to develop a rapid method for identification. DMS is used as a prefilter to separate positional isomers prior to subsequent identification by mass spectrometric analyses. Collision induced dissociation of each DMS separated species is used to reveal fragmentation patterns that can be correlated to specific isomer structures. Analysis of the experimental data and supporting quantum chemical calculations show that methylene-hydroxylated and methoxy-containing phenyl ring hydroxylated transformation products are observed. The proposed methodology outlines a high-throughput technique to determine transformation products of small molecules accurately, in a short time and requiring minimal sample concentrations (<25 ng/mL).