{
  "abstract": "Aging is a complex biological process involving multiple dysregulated pathways, and synergistic compound combinations offer distinct therapeutic advantages through multi-target and multi-pathway interactions. Traditional Chinese Medicine (TCM) formulations are inherently synergistic, yet systematically identifying their active anti-aging combinations remains a major challenge. Here, we developed DeepSMCA, a deep learning-based framework integrating molecular descriptors, ADMET parameters, and protein-protein interaction (PPI) network embeddings learned via a variational graph auto-encoder (VGAE), combined with a ResNetDNN classifier and the Bliss independence model, to identify synergistic combinations of anti-aging compound from the Dengzhan Shengmai (DZSM) formulation. Trained on 914 curated compounds, DeepSMCA achieved an area under the curve (AUC) of 0.9849 on the validation set, outperforming conventional machine learning and deep learning baselines, and interpretability analysis revealed that PPI network features contributed most (59.5%) to model predictions. Chemical profiling identified 30 constituents in DZSM, from which the three top-ranked synergistic combinations (Com1–3) were validated in D-galactose (D-Gal)-induced senescent PC12 cells. All three combinations enhanced cell viability, alleviated oxidative stress, attenuated intracellular reactive oxygen species accumulation, and decreased senescence-associated β-galactosidase-positive cells by up to 54.79%. Transcriptomic analysis showed that the combinations reversed 1,001–1,037 D-Gal-induced differentially expressed genes (DEGs), which were enriched in 18 shared aging-related pathways centered on longevity regulation, FoxO, p53, and autophagy signaling. Compound-target–aging-pathway network analysis further revealed complementary target engagement among constituents. This study establishes an interpretable, proof-of-concept computational–experimental pipeline for dissecting multi-component synergy in complex formulations, providing a generalizable strategy for anti-aging drug discovery from TCM.",
  "abstract_shown": true,
  "affiliations": [
    "Hebei Province Key Laboratory of Study and Exploitation of Chinese Medicine, Chengde Medical University, Chengde, China",
    "Chengde Medical University, Chengde, China",
    "Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing, China",
    "Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, Hebei, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jingyi Hou"
    },
    {
      "affs": [
        1
      ],
      "name": "Xueli Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Miao Gu"
    },
    {
      "affs": [
        3
      ],
      "name": "Kaikai Ding"
    },
    {
      "name": "Kailan Yang"
    },
    {
      "name": "Bowen Xu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fcd72csqevtbg.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0356888",
  "fulltext_url": null,
  "id": "fcd72csqevtbg",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0356888",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Bioenergetics/Energy-producing organelles/Mitochondria"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Cell processes/Cell death/Autophagic cell death"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Cellular structures and organelles/Energy-producing organelles/Mitochondria"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Oxidative stress"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Signal transduction/Cell signaling/Signaling cascades/MAPK signaling cascades"
    ],
    [
      "plos",
      "/Biology and life sciences/Developmental biology/Organism development/Aging/Senescence"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Physiological processes/Aging/Senescence"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Physiological processes/Homeostasis"
    ],
    [
      "plos",
      "/Computer and information sciences/Network analysis"
    ],
    [
      "plos",
      "/Computer and information sciences/Network analysis/Signaling networks"
    ]
  ],
  "openalex": "W7211929050",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0356888&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/fcd72csqevtbg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0356888",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0356888"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:cell biology",
      "subfield": "cell-biology"
    },
    {
      "field": "medicine",
      "origin": "plos:physiology",
      "subfield": "physiology"
    }
  ],
  "title": "Artificial intelligence-driven identification and mechanistic exploration of synergistic anti-aging compounds from Dengzhan Shengmai formulation",
  "title_text": "Artificial intelligence-driven identification and mechanistic exploration of synergistic anti-aging compounds from Dengzhan Shengmai formulation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/fcd72csqevtbg",
  "venue": "PLOS One",
  "versions": []
}