Unraveling potent Glycyrrhiza glabra flavonoids as AKT1 inhibitors using network pharmacology and machine learning-assisted QSAR.

IF 3.9 2区 化学 Q2 CHEMISTRY, APPLIED
Desu Gayathri Niharika, Punam Salaria, Amarendar Reddy M
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引用次数: 0

Abstract

Glycyrrhiza glabra (G. glabra) phytocompounds have been reported to interact with neurological targets, including those implicated in epilepsy, and may modulate epilepsy-related targets. While substantial evidence supports their potential antiepileptic effects, the underlying molecular mechanisms remain unclear. This study aims to elucidate the molecular mechanism of G. glabra phytocompounds by integrating network pharmacology and machine learning (ML)-based quantitative structure-activity relationship (QSAR) techniques. Network pharmacology analysis identified AKT1 as a key epilepsy-related target, and four ML-based 2D-QSAR models were developed using AKT1 inhibitors. The developed models underwent comprehensive validation, including internal and external validation, Y-randomization, statistical analysis, and applicability domain assessment to ensure robustness and reliability. Among them, the Multilayer Perceptron (MLP) model excelled as the most robust and demonstrated superior predictive ability with a correlation coefficient r2training = 0.95, r2test = 0.84, and cross-validation coefficient q2 = 0.72. The MLP model accurately predicted pIC50 values of phytoflavonoids, leading to the identification of 19 active molecules through the activity atlas model. ADME and drug-likeliness screening narrowed the selection to eleven promising compounds for further docking analysis. Molecular docking highlighted glabranin and 3'-hydroxy-4'-O-methylglabridin as top lead compounds with a binding energy of - 5.75 and - 5.37 kcal/mol, respectively. Additionally, 400 ns molecular dynamics simulation confirmed the structural and conformational stability of the glabranin-AKT1 complex, further reinforced by Principal Component Analysis, free energy landscapes, and Molecular Mechanics Poisson-Boltzmann/Generalized Born Surface Area. Collectively, these findings underscore the potential of G. glabra phytocompounds as promising antiepileptic candidates, paving the way for future advancements in this field.

利用网络药理学和机器学习辅助QSAR揭示强效甘草黄酮作为AKT1抑制剂。
据报道,光甘草(G. glabra)植物化合物与神经系统靶点相互作用,包括与癫痫有关的靶点,并可能调节癫痫相关靶点。虽然有大量证据支持其潜在的抗癫痫作用,但其潜在的分子机制尚不清楚。本研究旨在通过网络药理学和基于机器学习(ML)的定量构效关系(QSAR)技术相结合,阐明光草植物化合物的分子机制。网络药理学分析发现AKT1是一个关键的癫痫相关靶点,并利用AKT1抑制剂建立了四个基于ml的2D-QSAR模型。建立的模型进行了全面的验证,包括内部和外部验证、y随机化、统计分析和适用性领域评估,以确保稳健性和可靠性。其中,Multilayer Perceptron (MLP)模型鲁棒性最强,预测能力最强,相关系数r2training = 0.95, r2test = 0.84,交叉验证系数q2 = 0.72。MLP模型准确预测了植物类黄酮的pIC50值,从而通过活性图谱模型鉴定出19种活性分子。ADME和药物可能性筛选将选择范围缩小到11种有希望进行进一步对接分析的化合物。分子对接发现光甘草苷和3′-羟基-4′- o -甲基光甘草苷分别以- 5.75 kcal/mol和- 5.37 kcal/mol的结合能为顶级先导化合物。此外,400 ns分子动力学模拟证实了glabranin-AKT1配合物的结构和构象稳定性,并通过主成分分析、自由能景观和分子力学泊松-玻尔兹曼/广义Born表面积进一步增强了稳定性。总的来说,这些发现强调了光秃秃植物化合物作为有希望的抗癫痫候选药物的潜力,为该领域的未来发展铺平了道路。
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来源期刊
Molecular Diversity
Molecular Diversity 化学-化学综合
CiteScore
7.30
自引率
7.90%
发文量
219
审稿时长
2.7 months
期刊介绍: Molecular Diversity is a new publication forum for the rapid publication of refereed papers dedicated to describing the development, application and theory of molecular diversity and combinatorial chemistry in basic and applied research and drug discovery. The journal publishes both short and full papers, perspectives, news and reviews dealing with all aspects of the generation of molecular diversity, application of diversity for screening against alternative targets of all types (biological, biophysical, technological), analysis of results obtained and their application in various scientific disciplines/approaches including: combinatorial chemistry and parallel synthesis; small molecule libraries; microwave synthesis; flow synthesis; fluorous synthesis; diversity oriented synthesis (DOS); nanoreactors; click chemistry; multiplex technologies; fragment- and ligand-based design; structure/function/SAR; computational chemistry and molecular design; chemoinformatics; screening techniques and screening interfaces; analytical and purification methods; robotics, automation and miniaturization; targeted libraries; display libraries; peptides and peptoids; proteins; oligonucleotides; carbohydrates; natural diversity; new methods of library formulation and deconvolution; directed evolution, origin of life and recombination; search techniques, landscapes, random chemistry and more;
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