دورية أكاديمية
أعرض في EDS

Determining molecular properties with differential mobility spectrometry and machine learning.

التفاصيل البيبلوغرافية
العنوان: Determining molecular properties with differential mobility spectrometry and machine learning.
المؤلفون: Walker SWC; Department of Chemistry, University of Waterloo, Waterloo, N2L 3G1, ON, Canada., Anwar A; Department of Chemistry, University of Waterloo, Waterloo, N2L 3G1, ON, Canada., Psutka JM; Department of Chemistry, University of Waterloo, Waterloo, N2L 3G1, ON, Canada., Crouse J; Department of Chemistry, University of Waterloo, Waterloo, N2L 3G1, ON, Canada., Liu C; SCIEX, 71 Four Valley Drive, Concord, L4V 4V8, ON, Canada., Le Blanc JCY; SCIEX, 71 Four Valley Drive, Concord, L4V 4V8, ON, Canada., Montgomery J; Pfizer Global Research and Development, Eastern Point Road, Groton, 06340, CT, USA., Goetz GH; Pfizer Global Research and Development, Eastern Point Road, Groton, 06340, CT, USA., Janiszewski JS; Pfizer Global Research and Development, Eastern Point Road, Groton, 06340, CT, USA., Campbell JL; Department of Chemistry, University of Waterloo, Waterloo, N2L 3G1, ON, Canada. Larry.Campbell@sciex.com.; SCIEX, 71 Four Valley Drive, Concord, L4V 4V8, ON, Canada. Larry.Campbell@sciex.com., Hopkins WS; Department of Chemistry, University of Waterloo, Waterloo, N2L 3G1, ON, Canada. Scott.Hopkins@uwaterloo.ca.
المصدر: Nature communications [Nat Commun] 2018 Nov 30; Vol. 9 (1), pp. 5096. Date of Electronic Publication: 2018 Nov 30.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: [London] : Nature Pub. Group
مستخلص: 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.
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تواريخ الأحداث: Date Created: 20181204 Date Completed: 20181218 Latest Revision: 20200316
رمز التحديث: 20221213
مُعرف محوري في PubMed: PMC6269546
DOI: 10.1038/s41467-018-07616-w
PMID: 30504922
قاعدة البيانات: MEDLINE
الوصف
تدمد:2041-1723
DOI:10.1038/s41467-018-07616-w