Version 1
: Received: 13 June 2024 / Approved: 13 June 2024 / Online: 13 June 2024 (14:13:00 CEST)
Version 2
: Received: 14 June 2024 / Approved: 14 June 2024 / Online: 17 June 2024 (10:52:10 CEST)
How to cite:
Chihomvu, P.; Ganesan, A.; Gibbons, S.; Johannson, M.; Woollard, K.; Hayes, M. A. Phytochemicals in Drug Discovery - A Confluence of Tradition and Innovation. Preprints2024, 2024060927. https://doi.org/10.20944/preprints202406.0927.v1
Chihomvu, P.; Ganesan, A.; Gibbons, S.; Johannson, M.; Woollard, K.; Hayes, M. A. Phytochemicals in Drug Discovery - A Confluence of Tradition and Innovation. Preprints 2024, 2024060927. https://doi.org/10.20944/preprints202406.0927.v1
Chihomvu, P.; Ganesan, A.; Gibbons, S.; Johannson, M.; Woollard, K.; Hayes, M. A. Phytochemicals in Drug Discovery - A Confluence of Tradition and Innovation. Preprints2024, 2024060927. https://doi.org/10.20944/preprints202406.0927.v1
APA Style
Chihomvu, P., Ganesan, A., Gibbons, S., Johannson, M., Woollard, K., & Hayes, M. A. (2024). Phytochemicals in Drug Discovery - A Confluence of Tradition and Innovation. Preprints. https://doi.org/10.20944/preprints202406.0927.v1
Chicago/Turabian Style
Chihomvu, P., Kevin Woollard and Martin Allistair Hayes. 2024 "Phytochemicals in Drug Discovery - A Confluence of Tradition and Innovation" Preprints. https://doi.org/10.20944/preprints202406.0927.v1
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. 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.
Keywords
phytochemicals; natural products; traditional medicine
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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