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. 2022 May 27;23(11):6023.
doi: 10.3390/ijms23116023.

Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling

Amir Taldaev  1   2 , Roman Terekhov  2 , Ilya Nikitin  2 , Anastasiya Zhevlakova  2 , Irina Selivanova  2
Affiliations

Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling

Amir Taldaev et al. Int J Mol Sci. .

Abstract

Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.

Keywords: bias risk; cheminformatics; computer modeling; docking; flavonoids; in silico; limitations; molecular modeling; phytomedicine; systematic review.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Base structures of classical flavonoid groups.
Figure 2
Figure 2
PRISMA flowchart of the search and selection process of the articles.
Figure 3
Figure 3
Monitoring of scientific information on molecular modeling of flavonoids (Google Scholar data).
Figure 4
Figure 4
Interaction of taxifolin and P-glycoprotein.
Figure 5
Figure 5
Risk of bias graph.
Figure 6
Figure 6
Lead compounds.
Figure 7
Figure 7
The view of taxifolin nanoparticle (A) and its cross-section (B) [113].
See this image and copyright information in PMC

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