{
  "abstract": "African swine fever (ASF), caused by the African swine fever virus (ASFV), poses a catastrophic threat to global swine production due to the absence of effective vaccines or therapeutics. Targeting the virally encoded RNA polymerase (RNAP), which is essential for viral transcription, offers a promising antiviral strategy. Capitalizing on our previously resolved high-resolution structure of ASFV RNAP, we conducted a rigorous structure-based virtual screening of two compound libraries (T001 and LF1000) to identify selective inhibitors. From 100 candidates validated in vitro, nine compounds identified at the genome level by qPCR demonstrated >98% inhibition of ASFV replication in porcine alveolar macrophages (PAMs). The lead compound, K-7174 - a repurposed agent known to modulate proteasome and GATA activity - exhibited potent, dose-dependent antiviral efficacy (IC50 = 2.502 µmol/L), minimal cytotoxicity, and high binding affinity for ASFV RNAP. Molecular docking simulations indicated potential interactions with key catalytic residues, including Glu1051 and Ala456, proposing a mechanism that may involve active-site disruption. However, definitive confirmation of this binding pose requires further experimental validation through high-resolution structural methods such as cryo-EM. Our study not only nominates K-7174 as a promising anti-ASFV candidate but also establishes an integrated computational-experimental framework for accelerating the discovery of antiviral agents targeting viral polymerases.",
  "abstract_shown": true,
  "affiliations": [
    "State Key Laboratory for Animal Disease Control and Prevention, College of Veterinary Medicine, Lanzhou University, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou 730000, China.",
    "Gansu Province Research Center for Basic Disciplines of Pathogen Biology, Lanzhou 730046, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Bozhang Sun"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Fei Xi"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zhirui Liu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Huanan Liu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Haiyang Song"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Yifei Zhao"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zixiang Zhu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Haixue Zheng"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Guoliang Zhu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/46t60vg8t0avj.bib",
  "cited_by": 1,
  "doi": "10.52601/bpr.2025.250054",
  "fulltext_url": "https://europepmc.org/articles/PMC13740978",
  "id": "46t60vg8t0avj",
  "issns": [
    "2364-3439",
    "2364-3420"
  ],
  "landing_url": "https://europepmc.org/article/MED/42997079",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3002|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W4415463794",
  "pdf_url": "https://europepmc.org/articles/PMC13740978?pdf=render",
  "pmcid": "PMC13740978",
  "pmid": "42997079",
  "published": "2026-10-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/46t60vg8t0avj.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:34Z",
      "native_id": "MED:42997079",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42997079"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:3002|Computational Drug Discovery Methods",
      "subfield": "medicinal"
    }
  ],
  "title": "A computational-experimental strategy identifies K-7174 against African swine fever.",
  "title_text": "A computational-experimental strategy identifies K-7174 against African swine fever.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T03:20:49Z",
  "url": "https://research.draconitic.com/paper/46t60vg8t0avj",
  "venue": "Biophysics reports",
  "versions": []
}