Desu Gayathri Niharika, Punam Salaria, Amarendar Reddy M
{"title":"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。","authors":"Desu Gayathri Niharika, Punam Salaria, Amarendar Reddy M","doi":"10.1007/s11030-025-11210-w","DOIUrl":null,"url":null,"abstract":"<p><p>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 r<sup>2</sup><sub>training</sub> = 0.95, r<sup>2</sup><sub>test</sub> = 0.84, and cross-validation coefficient q<sup>2</sup> = 0.72. The MLP model accurately predicted pIC<sub>50</sub> 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.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unraveling potent Glycyrrhiza glabra flavonoids as AKT1 inhibitors using network pharmacology and machine learning-assisted QSAR.\",\"authors\":\"Desu Gayathri Niharika, Punam Salaria, Amarendar Reddy M\",\"doi\":\"10.1007/s11030-025-11210-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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 r<sup>2</sup><sub>training</sub> = 0.95, r<sup>2</sup><sub>test</sub> = 0.84, and cross-validation coefficient q<sup>2</sup> = 0.72. The MLP model accurately predicted pIC<sub>50</sub> 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.</p>\",\"PeriodicalId\":708,\"journal\":{\"name\":\"Molecular Diversity\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular Diversity\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1007/s11030-025-11210-w\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Diversity","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1007/s11030-025-11210-w","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
Unraveling potent Glycyrrhiza glabra flavonoids as AKT1 inhibitors using network pharmacology and machine learning-assisted QSAR.
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.
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;