Evidence map›Paper›PMID 39777110›Full record

ReviewBioMedicine2024

Integrating natural product research laboratory with artificial intelligence: Advancements and breakthroughs in traditional medicine.

Jai-Sing Yang, Shih-Chang Tsai, Yuan-Man Hsu, Da-Tian Bau, Chia-Wen Tsai, Wen-Shin Chang, Sheng-Chu Kuo, Chien-Chih Yu, Yu-Jen Chiu, Fuu-Jen Tsai

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BioMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Potential ofPharmaceuticals (Basel, Switzerland) · 2025
    Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jai-Sing Yang *Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Shih-Chang Tsai *Department of Biological Science and Technology, China Medical University, Taichung, Taiwan.
Yuan-Man HsuDepartment of Biological Science and Technology, China Medical University, Taichung, Taiwan.
Da-Tian BauDepartment of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Chia-Wen TsaiDepartment of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Wen-Shin ChangDepartment of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Sheng-Chu KuoDepartment of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Chien-Chih YuSchool of Pharmacy, College of Pharmacy, China Medical University, Taichung, Taiwan.
Yu-Jen ChiuDivision of Plastic and Reconstructive Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan.
Fuu-Jen TsaiMillion-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Natural Product Research Laboratory (NPRL) of China Medical University Hospital (CMUH) was established in collaboration with CMUH and Professor Kuo-Hsiung Lee from the University of North Carolina at Chapel Hill. The laboratory collection features over 6000 natural products worldwide, including pure compounds and semi-synthetic derivatives. This is the most comprehensive and fully operational natural product database in Taiwan. This review article explores the history and development of the NPRL of CMUH. We then provide an overview of the recent applications and impact of artificial intelligence (AI) in new drug discovery. Finally, we examine advanced powerful AI-tools and related software to explain how these resources can be utilized in research on large-scale drug data libraries. This article presents a drug research and development (R&D) platform that combines AI with the NPRL. We believe that this approach will reduce resource wastage and enhance the research capabilities of Taiwan's academic and industrial sectors in biotechnology and pharmaceuticals.

Indexed as

Artificial Intelligence (AI)Drug discoveryDrug research and development (R&D)Natural productsNatural Products Research Laboratories (NPRL)

Identifiers

PMID39777110
PMCPMC11703400

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.