{
  "abstract": "Mass spectrometry resolution of glycosphingolipids requires advanced separation methods. The virtual-ly identical structures of glycolipid epimers cannot be discriminated by routine multiple reaction monitoring-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (MRM-LC-ESI-MS/MS). Sep-aration can be achieved using field asymmetric (differential) ion mobility mass spectrometry (FAIMS/DMS). A major bottleneck to MRM-LC-ESI-DMS-MS/MS method development lies in manually optimizing the compound-dependent parameters of separation voltage (SV) and compensation voltage (CoV). To address this challenge, we developed intelligent Differential Mobility Spectrometry (iDMS). iDMS is a supervised deep neural network that evaluates and predicts the optimal ion mobility SV and CoV parameters for any user-requested monoglycosphingolipid from a training set of measured signal intensities composed of 12 lipids (6 stereoisomer pairs). We initially introduced iDMS as a series of python scripts. Here, we share desktop applications of both iDMS and its pre-processing input data assembly modules (DMS Data Extraction Toolkit). Both software assemblies enable users to rapidly deploy a validated machine learning alternative to manual DMS optimization. The applications are user-friendly, operate with-out need for any technical operating environment, make training dataset assembly quick and simple, and return measured, normalized, and predicted ionograms at every user-interrogated SV, evaluation of separability at each SV and CoV combination, and prediction of optimal resolution parameters to accelerate users MRM-LC-ESI-DMS-MS/MS deployment. Availability and implementation: Desktop iDMS and DMS Data Extraction Toolkit are native desktop applications for macOS (Apple Silicon), freely available for download at https://www.neurolipidomics.com/platforms.html under the GNU v3.0 license. The source code is available from GitHub and Zenodo at https://github.com/Neurolipidomics/iDMS, https://zenodo.org/records/22214408, https://github.com/Neurolipidomics/DMSDataExtractionToolkit, and https://zenodo.org/records/22777403. Bench-marking metrics are available at https://www.biorxiv.org/content/10.64898/2026.08.26.747394v1.",
  "abstract_shown": true,
  "affiliations": [
    "University of Ottawa"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "T. Nguyen-Tran"
    },
    {
      "name": "E. Hashimoto-Roth"
    },
    {
      "name": "X.-X. Shi"
    },
    {
      "name": "M. G. Organ"
    },
    {
      "name": "T. J. Perkins"
    },
    {
      "name": "M. Lavallee-Adam"
    },
    {
      "affs": [
        0
      ],
      "name": "S. A. L. Bennett"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7y5y95dgz5c7j.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.02.756329",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.02.756329v1.full",
  "id": "7y5y95dgz5c7j",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.02.756329v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1607|Mass Spectrometry Techniques and Applications"
    ]
  ],
  "openalex": "W7220856551",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.02.756329v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7y5y95dgz5c7j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:27Z",
      "native_id": "10.64898/2026.10.02.756329",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.02.756329v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Desktop iDMS: A stand-alone application of intelligent differential mobility spectrometry (iDMS), a neural net-work that predicts optimal separation and compensa-tion voltages for field asymmetric differential ion mobil-ity spectrometry",
  "title_text": "Desktop iDMS: A stand-alone application of intelligent differential mobility spectrometry (iDMS), a neural net-work that predicts optimal separation and compensa-tion voltages for field asymmetric differential ion mobil-ity spectrometry",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:27Z",
  "url": "https://research.draconitic.com/paper/7y5y95dgz5c7j",
  "venue": "bioRxiv",
  "versions": []
}