Evidence map›Paper›PMID 39201478›Full record

ReviewInternational journal of molecular sciences2024

Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation.

Patience Chihomvu, A Ganesan, Simon Gibbons, Kevin Woollard, Martin A Hayes

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing 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

48 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Plants (Basel, Switzerland) · 2026
    Review
  9. Article
  10. Review
  11. Review
  12. Food science & nutrition · 2026
    Article
  13. Development and Characterization ofLife (Basel, Switzerland) · 2026
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Tropical Almond Tree (Pharmaceuticals (Basel, Switzerland) · 2026
    Review
  19. Review
  20. Review
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

5 authors.

Patience ChihomvuCompound Synthesis and Management, Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, 431 83 Mölndal, Sweden.
A GanesanSchool of Chemistry, Pharmacy & Pharmacology, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK.ORCID 0000-0003-4862-7999
Simon GibbonsNatural and Medical Sciences Research Center, University of Nizwa, Birkat Al Mawz 616, Oman.
Kevin WoollardBioscience Renal, Research and Early Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB21 6GH, UK.
Martin A HayesCompound Synthesis and Management, Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, 431 83 Mölndal, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. An emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

Indexed as

Drug DiscoveryPhytochemicalsQuantitative Structure-Activity RelationshipBiological ProductsHumansMachine LearningMolecular Docking SimulationBiological ProductsPhytochemicalsnatural productsphytochemicalstraditional medicine

Identifiers

PMID39201478
PMCPMC11354359

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.