{
  "abstract": "Natural products, owing to their unique biological activities, possess the ability to interact with specific target proteins or regulatory networks, representing a valuable source of innovative drug candidates. However, target identification remains a major bottleneck in natural product-based drug discovery, largely because of the chemical complexity of natural products and the heterogeneity of biological systems. To address these challenges, various complementary strategies have been developed, including experimental strategies such as chemical proteomics, and computational methods such as artificial intelligence-driven methods. Nevertheless, reliably advancing a candidate protein hit to a therapeutically relevant and physiologically validated target remains a critical challenge. Focusing on technologies for natural product target discovery, this review systematically summarizes the principles, methodologies, and practical applications of current approaches. Through representative case studies, we further propose a reusable integrated experimental-computational workflow and illustrates how key targets and their modes of action can be identified in real-world research scenarios. In addition, we discuss common technical and conceptual bottlenecks encountered during target discovery and proposes potential countermeasures. The review provides an actionable reference framework for natural product target identification, with the goal of reducing false-positive findings and fragmented evidence, thereby improving the robustness of mechanism-oriented studies and facilitating subsequent translational research.",
  "abstract_shown": true,
  "affiliations": [
    "Shanghai Frontiers Science Center of TCM Chemical Biology Institute of Interdisciplinary Integrative Medicine Research and Shuguang Hospital Shanghai University of Traditional Chinese Medicine Shanghai China",
    "State Key Laboratory of Drug Research Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai China",
    "Department of Phytochemistry School of Pharmacy Second Military Medical University Shanghai China",
    "State Key Laboratory For Quality Ensurance and Sustainable Use of Dao‐di Herbs Institute of Medicinal Plant Development Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China"
  ],
  "article_type": "review",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Qiyuan Pan",
      "orcid": "0009-0005-2567-3206"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiao Yuan",
      "orcid": "0009-0008-5940-7602"
    },
    {
      "affs": [
        0
      ],
      "name": "Jinmei Jin",
      "orcid": "0000-0002-5948-5001"
    },
    {
      "affs": [
        0
      ],
      "name": "Xin Luan",
      "orcid": "0000-0003-3674-256X"
    },
    {
      "affs": [
        1
      ],
      "name": "Cheng Luo",
      "orcid": "0000-0003-3864-8382"
    },
    {
      "affs": [
        0
      ],
      "name": "Hongzhuan Chen",
      "orcid": "0000-0002-5367-2949"
    },
    {
      "affs": [
        2,
        0,
        3
      ],
      "name": "Weidong Zhang",
      "orcid": "0000-0002-2433-6823"
    },
    {
      "affs": [
        0
      ],
      "name": "Haitao Zhang",
      "orcid": "0000-0002-1639-4089"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0qpssr151915g.bib",
  "cited_by": 5,
  "doi": "10.1002/mco2.70777",
  "fulltext_url": null,
  "id": "0qpssr151915g",
  "issns": [
    "2688-2663"
  ],
  "landing_url": "https://doi.org/10.1002/mco2.70777",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3002|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7162304914",
  "pdf_url": null,
  "pmcid": "PMC13239285",
  "pmid": "42253922",
  "published": "2026-05-24",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/0qpssr151915g.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:21:00Z",
      "native_id": "W7162304914",
      "source": "openalex",
      "url": "https://doi.org/10.1002/mco2.70777"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:3002|Computational Drug Discovery Methods",
      "subfield": "medicinal"
    }
  ],
  "title": "Progress in Natural Products Target Discovery Technology",
  "title_text": "Progress in Natural Products Target Discovery Technology",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:17Z",
  "url": "https://research.draconitic.com/paper/0qpssr151915g",
  "venue": "MedComm",
  "versions": []
}