ArticleNature communications2018
Determining molecular properties with differential mobility spectrometry and machine learning.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Building Explainable Graph Neural Network by Sparse Learning for the Drug-Protein Binding Prediction.Journal of computational biology : a journal of computational molecular cell biology · 2025Article
- Determining the gas-phase structures of α-helical peptides from shape, microsolvation, and intramolecular distance data.Nature communications · 2023Article
- Machine learning and signal processing assisted differential mobility spectrometry (DMS) data analysis for chemical identification.Analytical methods : advancing methods and applications · 2022Article
- Regulatory perspectives of combination products.Bioactive materials · 2022Review
- Feature Importance of Acute Rejection among Black Kidney Transplant Recipients by Utilizing Random Forest Analysis: An Analysis of the UNOS Database.Medicines (Basel, Switzerland) · 2021Article
- Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles.Science advances · 2021Article
- Separating chiral isomers of amphetamine and methamphetamine using chemical derivatization and differential mobility spectrometry.Analytical science advances · 2020Article
- Preferential Ion Microsolvation in Mixed-Modifier Environments Observed Using Differential Mobility Spectrometry.Journal of the American Society for Mass Spectrometry · 2019Article
- Separation of Sialylated Glycan Isomers by Differential Mobility Spectrometry.Analytical chemistry · 2019Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.