{
  "abstract": "Collision cross section (CCS) measurements from ion mobility-mass spectrometry improve confidence in molecular identification in metabolomics and lipidomics and reduce false-positive annotations in untargeted analyses. However, experimental CCS coverage remains incomplete, and accurate computational prediction remains needed. Here, we present Deep3DCCS, a geometry-driven deep learning framework that predicts CCS from stacked multi-angle molecular projection tensors using a three-dimensional convolutional neural network operating across the projection-view and two spatial image dimensions. Three-dimensional molecular structures generated from SMILES strings were rotated around the x, y, and z-axes, converted into binary two-dimensional projections, and assembled into standardized multi-view tensors for CCS regression. Deep3DCCS was developed and evaluated using curated AllCCS and METLIN lipid CCS datasets containing 3948 molecule-adduct entries across [M - H]-, [M + H]+, and [M + Na]+ ions, and input optimization identified 32 × 32 pixel projections with five rotations per axis as a practical balance between prediction accuracy, model stability, and computational feasibility. In independent true-validation experiments, AllCCS-trained models achieved MRPE values of 3.27-3.79% on AllCCS validation data and 2.93-3.72% on METLIN validation data, whereas METLIN-trained models transferred less effectively to the broader AllCCS chemical space. Overall, these results support multi-view molecular projection tensors as a viable geometry-driven representation for CCS prediction and demonstrate that substantial CCS-relevant information can be learned directly from three-dimensional molecular morphology using a 3DCNN architecture, with low single-digit prediction errors across independent and cross-dataset validation experiments.",
  "abstract_shown": true,
  "affiliations": [
    "Siriraj Center of Research Excellence in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok10700, Thailand.",
    "Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok10700, Thailand.",
    "Thailand Metabolomics Society, Bangkok10700, Thailand.",
    "Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok10700, Thailand.",
    "Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok10700, Thailand.",
    "Center of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science Mahidol University, Bangkok10400, Thailand."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Pattipong Wisanpitayakorn"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Vivek Bhakta Mathema"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Kwanjeera Wanichthanarak",
      "orcid": "0000-0003-0245-0833"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Thanyanatthamon Keawhawong"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Narumol Jariyasopit",
      "orcid": "0000-0002-7702-1323"
    },
    {
      "affs": [
        3
      ],
      "name": "Pairash Saiviroonporn"
    },
    {
      "affs": [
        3
      ],
      "name": "Trongtum Tongdee"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Yongyut Sirivatanauksorn"
    },
    {
      "affs": [
        0,
        1,
        2,
        4,
        5
      ],
      "name": "Sakda Khoomrung",
      "orcid": "0000-0001-9461-8597"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/36d3ewe2zexk9.bib",
  "cited_by": 0,
  "doi": "10.1021/acs.jcim.6c00371",
  "fulltext_url": "https://europepmc.org/articles/PMC13625951",
  "id": "36d3ewe2zexk9",
  "issns": [
    "1549-9596",
    "1549-960X"
  ],
  "landing_url": "https://europepmc.org/article/MED/42906041",
  "language": "eng",
  "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": [
    [
      "openalex",
      "1607|Mass Spectrometry Techniques and Applications"
    ]
  ],
  "openalex": "W7211878962",
  "pdf_url": "https://europepmc.org/articles/PMC13625951?pdf=render",
  "pmcid": "PMC13625951",
  "pmid": "42906041",
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/36d3ewe2zexk9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:57Z",
      "native_id": "MED:42906041",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42906041"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:1607|Mass Spectrometry Techniques and Applications",
      "subfield": "physical"
    }
  ],
  "title": "Deep3DCCS: Geometry-Driven Collision Cross Section Prediction from Multi-View Molecular Projection Tensors.",
  "title_text": "Deep3DCCS: Geometry-Driven Collision Cross Section Prediction from Multi-View Molecular Projection Tensors.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:57Z",
  "url": "https://research.draconitic.com/paper/36d3ewe2zexk9",
  "venue": "Journal of chemical information and modeling",
  "versions": []
}