Determining molecular properties with differential mobility spectrometry and machine learning.

ABSTRACT: 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 ga

SUBMITTER: Walker SWC 

PROVIDER: S-EPMC6269546 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature