{
  "abstract": "Background: Chronic low-grade inflammation underlies numerous chronic diseases, yet the molecular targets of dietary bioactive compounds remain largely unknown. Methods: We developed a deep learning model integrating heterogeneous network propagation, stacked denoising autoencoders, and a CNN classifier to predict inflammation-related targets of food-derived compounds. The model was validated by cross-validation and on eight compounds from distinct structural categories. Avn A, Avn B, and Avn C were then input into the model to predict their anti-inflammatory targets, followed by network analysis, docking, and cellular assays for validation. Results: The model achieved a mean AUC of 0.9587 and an AUPR of 0.9590, with validation on eight structurally diverse compounds confirming its generalizability. For Avn A, Avn B, and Avn C, 118 overlapping anti-inflammatory targets were predicted. Network analysis and KEGG pathway enrichment identified ERK1/2 as a key node within the MAPK and PI3K-AKT signaling pathways, while COX-2 was prioritized for further validation based on its high prediction score. Molecular docking revealed that Avns bound at the cyclooxygenase channel entrance of COX-2 with binding energies of −7.7 to −8.0 kcal/mol, and at the ATP-binding pockets of ERK1/2 with binding energies of −7.0 to −7.7 kcal/mol. In LPS-stimulated macrophages, Avns dose-dependently inhibited COX-2 enzymatic activity and PGE2 production, attenuated ERK1/2 and c-Fos phosphorylation, and suppressed IL-6 and IL-1β expression. Conclusions: Our deep learning model successfully identified COX-2 and ERK1/2 as key mediators of the anti-inflammatory effects of Avns, revealing a dual mechanism involving COX-2 inhibition and ERK/c-Fos pathway blockade. This study provides new evidence for the molecular targets of Avns and demonstrates the utility of deep learning-based approaches for target discovery of dietary bioactive compounds.",
  "abstract_shown": true,
  "affiliations": [
    "Key Laboratory of Geriatric Nutrition and Health, Ministry of Education, Beijing Technology and Business University, Beijing 100048, China",
    "China-Canada Joint Lab of Food Nutrition and Health, Beijing Technology and Business University, Beijing 100048, China",
    "Key Laboratory of Special Food Supervision Technology for State Market Regulation, Beijing Technology and Business University, Beijing 100048, China",
    "School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Jingjie Zhang",
      "orcid": "0000-0002-3343-0571"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Tengyu Li"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Chi Yan"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Zeran Wang"
    },
    {
      "affs": [
        3
      ],
      "name": "Zhiyao Zhao",
      "orcid": "0000-0001-8565-4430"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Yonghui Yu"
    },
    {
      "affs": [
        3
      ],
      "name": "Ding Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/81hzd5ptmqfm1.bib",
  "cited_by": 0,
  "doi": "10.3390/nu18203339",
  "fulltext_url": "https://europepmc.org/articles/PMC13708514",
  "id": "81hzd5ptmqfm1",
  "issns": [
    "2072-6643"
  ],
  "landing_url": "https://www.mdpi.com/2072-6643/18/20/3339",
  "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": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7221562050",
  "pdf_url": "https://europepmc.org/articles/PMC13708514?pdf=render",
  "pmcid": "PMC13708514",
  "pmid": "42942909",
  "published": "2026-10-12",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/81hzd5ptmqfm1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:56Z",
      "native_id": "10.3390/nu18203339",
      "source": "crossref",
      "url": "https://www.mdpi.com/2072-6643/18/20/3339"
    },
    {
      "first_seen": "2026-10-30T12:22:12Z",
      "native_id": "MED:42942909",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42942909"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2072-6643",
      "subfield": "nutrition"
    }
  ],
  "title": "Deep Learning-Driven Identification and Experimental Validation of COX-2 and ERK1/2 as Anti-Inflammatory Targets of Avenanthramides",
  "title_text": "Deep Learning-Driven Identification and Experimental Validation of COX-2 and ERK1/2 as Anti-Inflammatory Targets of Avenanthramides",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:12Z",
  "url": "https://research.draconitic.com/paper/81hzd5ptmqfm1",
  "venue": "Nutrients",
  "versions": []
}