Evidence map›Paper›PMID 37486499›Full record

ArticleAdvances in experimental medicine and biology2023

3D QSAR based Virtual Screening of Flavonoids as Acetylcholinesterase Inhibitors.

Sowmya Andole, Husna Sd, Srija Sudhula, Lavanya Vislavath, Hemanth Kumar Boyina, Kiran Gangarapu, Vasudha Bakshi, Krishna Prasad Devarakonda

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Article in Advances in experimental medicine and biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Sowmya AndoleSchool of Pharmacy, Department of Pharmacology, Anurag University, Hyderabad, Telangana, India.
Husna SdSchool of Pharmacy, Department of Pharmacology, Anurag University, Hyderabad, Telangana, India.
Srija SudhulaSchool of Pharmacy, Department of Pharmacology, Anurag University, Hyderabad, Telangana, India.
Lavanya VislavathSchool of Pharmacy, Department of Pharmacology, Anurag University, Hyderabad, Telangana, India.
Hemanth Kumar BoyinaSchool of Pharmacy, Department of Pharmacology, Anurag University, Hyderabad, Telangana, India.
Kiran GangarapuSchool of Pharmacy, Department of Pharmaceutical Analysis, Anurag University, Hyderabad, Telangana, India.
Vasudha BakshiSchool of Pharmacy, Department of Pharmacology, Anurag University, Hyderabad, Telangana, India.
Krishna Prasad DevarakondaSchool of Pharmacy, Department of Pharmacology, Anurag University, Hyderabad, Telangana, India. krishnaprasadpharmacy@anurag.edu.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In an attempt to develop therapeutic agents to treat Alzheimer's disease, a series of flavonoid analogues were collected, which already had established acetylcholinesterase (AChE) enzyme inhibition activity. For each molecule we also collected biological activity data (Ki). Then, 3D-QSAR (quantitative structure-activity relationship model) was developed which showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 and q2. This SAR data can explain the key descriptors which can be related to AChE inhibitory activity. Using the QSAR model, pharmacophores were developed based on which, virtual screening was done and a dataset was obtained which loaded as a prediction set to fit the developed QSAR model. Top 10 compounds fitting the QSAR model were subjected to molecular docking. CHEMBL1718051 was found to be the lead compound. This study is offering an example of a computationally-driven tool for prioritisation and discovery of probable AChE inhibitors. Further, in vivo and in vitro testing will show its therapeutic potential.

Indexed as

Cholinesterase InhibitorsQuantitative Structure-Activity RelationshipAcetylcholinesteraseFlavonoidsMolecular Docking SimulationAcetylcholinesteraseCholinesterase InhibitorsFlavonoids3DQSARAcetylcholinesteraseAlzheimer’s diseaseCressetMolecular docking

Identifiers

PMID37486499

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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.