{
  "abstract": "Anaplastic lymphoma kinase (ALK) is a key therapeutic target for various cancers because of its central role in tumor growth and survival signaling. This study employed an integrated computational workflow combining machine learning (ML), molecular docking, molecular dynamics (MD) simulations, umbrella sampling (US), and toxicity prediction to identify potential ALK inhibitors from 307 phytochemicals of the Glycosmis genus. A previously validated CatBoost regression model predicted binding free energies, leading to the selection of 26 compounds with ΔGML < −11 kcal mol−1. Molecular docking identified six top candidates with favorable binding affinities ranging from −5.856 to −9.667 kcal mol⁻¹, which subsequently underwent 200 ns MD simulations. Structural analyses confirmed the stability of all ALK–ligand complexes, while interaction studies revealed extensive hydrophobic contacts and conserved hydrogen bonds involving key ALK residues. US calculations estimated binding free energies from −8.354 to −13.272 kcal mol−1, with compound 54 exhibiting the strongest affinity (ΔGUS = −13.272 kcal mol−1). Several alkaloids, including acrifoline, heptazoline, and atalaphyllidine, also demonstrated promisingbinding properties and reported anticancer activities. Toxicity prediction indicated acceptable safety profiles, highlighting Glycosmis-derived phytochemicals as promising potential ALK inhibitor candidates and supporting the utility of ML-guided virtual screening combined with advanced molecular simulations for natural product-based anticancer drug discovery. Nevertheless, biochemical and cellular experiments are required to confirm their ALK inhibitory activity, selectivity, and therapeutic potential.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Chemistry, Vietnam Academy of Science and Technology (VAST) 1 , 18 Hoang Quoc Viet, Nghia Do, Hanoi, Vietnam",
    "Department of Chemistry, Vinh University 2 , 182 Le Duan, Truong Vinh, Nghean 43000, Vietnam",
    "Faculty of Chemistry and Center for Computational Science, Hanoi National University of Education 3 , 136 Xuan Thuy, Hanoi 10000, Vietnam",
    "Institute of Materials Science, VAST 4 , 18 Hoang Quoc Viet, Hanoi 10000, Vietnam",
    "Institute of Biology, Chemistry and Environment, Vinh University 5 , 182 Le Duan, Truong Vinh, Nghean 43000, Vietnam"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xuan Ha Nguyen"
    },
    {
      "affs": [
        1
      ],
      "name": "Xuan Duc Dau"
    },
    {
      "affs": [
        2
      ],
      "name": "Thi Minh Hue Nguyen"
    },
    {
      "affs": [
        2
      ],
      "name": "Ngoc Dung Tran"
    },
    {
      "affs": [
        3
      ],
      "name": "Van Trang Nguyen"
    },
    {
      "affs": [
        1
      ],
      "name": "Le Binh Doan"
    },
    {
      "affs": [
        4
      ],
      "name": "Thi My Chau Le"
    },
    {
      "affs": [
        1
      ],
      "name": "Thuy Phan Thi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ebvef0nm6mvfy.bib",
  "cited_by": 0,
  "doi": "10.1098/rsos.261178",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13730657/?tool=EBI",
  "id": "ebvef0nm6mvfy",
  "issns": [
    "2054-5703"
  ],
  "landing_url": "https://royalsocietypublishing.org/rsos/article/13/11/261178/484015/Discovery-of-potential-ALK-inhibitors-from",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Biochemistry"
    ],
    [
      "heading",
      "Chemistry"
    ],
    [
      "heading",
      "Computer Modelling and Simulation"
    ],
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7225176690",
  "pdf_url": "https://royalsocietypublishing.org/rsos/article-pdf/doi/10.1098/rsos.261178/6175297/rsos.261178.pdf",
  "pmcid": "PMC13730657",
  "pmid": "42979631",
  "published": "2026-11-04",
  "publisher": "The Royal Society",
  "ris": "https://research.draconitic.com/paper/ebvef0nm6mvfy.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T12:20:47Z",
      "native_id": "10.1098/rsos.261178",
      "source": "crossref",
      "url": "https://royalsocietypublishing.org/rsos/article/13/11/261178/484015/Discovery-of-potential-ALK-inhibitors-from"
    },
    {
      "first_seen": "2026-11-04T15:20:45Z",
      "native_id": "PMC:PMC13730657",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13730657"
    },
    {
      "first_seen": "2026-11-07T12:23:10Z",
      "native_id": "PMC13730657",
      "source": "pmc",
      "url": "https://doi.org/10.1098/rsos.261178"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "heading:Biochemistry",
      "subfield": "general"
    },
    {
      "field": "chemistry",
      "origin": "heading:Chemistry",
      "subfield": "general"
    }
  ],
  "title": "Discovery of potential ALK inhibitors from Glycosmis phytochemicals via integrated machine learning and umbrella sampling approaches",
  "title_text": "Discovery of potential ALK inhibitors from Glycosmis phytochemicals via integrated machine learning and umbrella sampling approaches",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:23:10Z",
  "url": "https://research.draconitic.com/paper/ebvef0nm6mvfy",
  "venue": "Royal Society Open Science",
  "versions": []
}