2D-QSAR model development and analysis on variant groups of anti -tuberculosis drugs

Dwivedi, Neeraja; Mishra, Bhartendu Nath; Katoch, Vishwa Mohan

doi:10.6026/97320630007082

Cited by 16 publications

(7 citation statements)

References 23 publications

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“…90) and activity prediction accuracy of 81% (R² cv = 0.81). Initially, a total of 20 derivatives were used for Quantitative structure-activity relationship (QSAR) modeling against physicochemical descriptors [18]. According to the quantitative structure-activity relationship (QSAR) equation 1 (table 3) is dependent on logP, the heat of formation, and molar refractivity for the antifungal activity against Candida albicans.…”

Section: Resultsmentioning

confidence: 99%

2d-Qsar Study on Some Novel Dihydropyrimidine-4-Carbonitrile Analogs as an Antifungal Activity

PATEL¹,

PASHA²,

Patel³

2023

Int J Pharm Pharm Sci

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Objective: The present study was designed to study the antifungal activity of Dihydropyrimidine-4-Carbonitrile analogs against the fungi Candida albicans by a 2D quantitative structure-activity relationship (QSAR) model. Methods: The pyrimidine derivatives were produced using lipophilic, electronic, and steric parameters by Quantitative Structure Activity-Relationships (QSAR). A relationship between dependent and independent variables (biological activities and physicochemical descriptors, respectively) was resolved statistically using regression analysis. The F value shows the level of statistical significance of the regression (r2) was used to report the fitness of data. The newly synthesized derivatives were evaluated for in vitro antifungal activity against Candida albicansby Nutrient agar and Seaboard dextrose agar media. Results: Multiple linear regression is a method of crucial importance, it allowed us to obtain a relation between the calculated parameters and the antifungal activity; this we can interpret the variance of the activity by contribution to the calculated descriptors. Quantitative structure-activity relationship (QSAR) model showing a significant activity-descriptors relationship accuracy of 90% (R2 ≥ 0.90) and activity prediction accuracy of 81% (R²cv = 0.81). These values prove that the model obtained is reliable. Out of the three descriptors studied; log P has minimum potency, molar refractivity has more potency and heat of formation has moderate potency. Conclusion: Important structural understanding in the pattern of potent antifungal agents by Quantitative Structure Activity-Relationships (QSAR) study. The acquired physicochemical properties (electronic, topological, and steric) show the important structural features required for antifungal activity against Candida albicans.

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Section: Resultsmentioning

confidence: 99%

2d-Qsar Study on Some Novel Dihydropyrimidine-4-Carbonitrile Analogs as an Antifungal Activity

PATEL¹,

PASHA²,

Patel³

2023

Int J Pharm Pharm Sci

View full text Add to dashboard Cite

show abstract

“…A variety of in vitro and in vivo assays have been proposed to find novel therapeutic agents targeting various putative molecular targets ( vide supra ) for T2DM treatment [ 278 ]. Drug discovery processes are important and include variety of activities using assay models (in vitro, in vivo, and in silico) [ 279 ]. In vitro and in vivo tests for T2DM can also evaluate the toxicity of drugs or compounds in development and the modified activity, and bioavailability of herbal medicine compounds [ 280 ].…”

Section: Comparing In Vitro (Enzymatic Cellular) and In Vivo Advantages And Disadvantages Of In Silico Modeling Applications In T2dm)mentioning

confidence: 99%

In Silico Approaches to Identify Polyphenol Compounds as α-Glucosidase and α-Amylase Inhibitors against Type-II Diabetes

Riyaphan¹,

Pham

Leong

et al. 2021

Biomolecules

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Type-II diabetes mellitus (T2DM) results from a combination of genetic and lifestyle factors, and the prevalence of T2DM is increasing worldwide. Clinically, both α-glucosidase and α-amylase enzymes inhibitors can suppress peaks of postprandial glucose with surplus adverse effects, leading to efforts devoted to urgently seeking new anti-diabetes drugs from natural sources for delayed starch digestion. This review attempts to explore 10 families e.g., Bignoniaceae, Ericaceae, Dryopteridaceae, Campanulaceae, Geraniaceae, Euphorbiaceae, Rubiaceae, Acanthaceae, Rutaceae, and Moraceae as medicinal plants, and folk and herb medicines for lowering blood glucose level, or alternative anti-diabetic natural products. Many natural products have been studied in silico, in vitro, and in vivo assays to restrain hyperglycemia. In addition, natural products, and particularly polyphenols, possess diverse structures for exploring them as inhibitors of α-glucosidase and α-amylase. Interestingly, an in silico discovery approach using natural compounds via virtual screening could directly target α-glucosidase and α-amylase enzymes through Monte Carto molecular modeling. Autodock, MOE-Dock, Biovia Discovery Studio, PyMOL, and Accelrys have been used to discover new candidates as inhibitors or activators. While docking score, binding energy (Kcal/mol), the number of hydrogen bonds, or interactions with critical amino acid residues have been taken into concerning the reliability of software for validation of enzymatic analysis, in vitro cell assay and in vivo animal tests are required to obtain leads, hits, and candidates in drug discovery and development.

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“…Because GFA provided a better fit to the training set, it was also commonly used for QSAR modeling ( Wang et al, 2012 ). The pIC 50 value is typically used to describe the biological activity of a substance, The “calculate molecular properties” module was used to calculate a number of molecular descriptors included AlogP, molecular weight, the total number of bonds, the minimum energy of conformation (kcal/mol), volume, surface area, and other properties ( Dwivedi et al, 2011 ; Imran et al, 2015 ). Some statistical parameters, such as the coefficient of determination ( r 2 ), adjusted r 2 (r 2 adj), and prediction (PRESS) r 2 ( r 2 pred), determine the accuracy of our constructed model.…”

Section: Methodsmentioning

confidence: 99%

Study on the molecular mechanism of anti-liver cancer effect of Evodiae fructus by network pharmacology and QSAR model

Chen

Han

2023

Front. Chem.

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Introduction: Evodiae Fructus (EF) is the dried, near ripe fruit of Euodia rutaecarpa (Juss.) Benth in Rutaceae. Numerous studies have demonstrated its anti-liver cancer properties. However, the molecular mechanism of Evodiae fructus against liver cancer and its structure-activity connection still require clarification.Methods: We utilized network pharmacology and a QSAR (2- and 3-dimensional) model to study the anti-liver cancer effect of Evodiae fructus. First, by using network pharmacology to screen the active substances and targets of Evodiae fructus, we investigated the signaling pathways involved in the anti-liver cancer actions of Evodiae fructus. The 2D-QSAR pharmacophore model was then used to predict the pIC50 values of compounds. The hiphop method was used to create an ideal 3D-QSAR pharmacophore model for the prediction of Evodiae fructus compounds. Finally, molecular docking was used to validate the rationality of the pharmacophore, and molecular dynamics was used to disclose the stability of the compounds by assessing the trajectories in 10 ns using RMSD, RMSF, Rg, and hydrogen bonding metrics.Results: In total, 27 compounds were acquired from the TCMSP and TCM-ID databases, and 45 intersection targets were compiled using Venn diagrams. Network integration analysis was used in this study to identify SRC as a primary target. Key pathways were discovered by KEGG pathway analysis, including PD-L1 expression and PD-1 checkpoint pathway, EGFR tyrosine kinase inhibitor resistance, and ErbB signaling pathway. Using a 2D-QSAR pharmacophore model and the MLR approach to predict chemical activity, ten highly active compounds were found. Two hydrophobic features and one hydrogen bond acceptor feature in the 3D-QSAR pharmacophore model were validated by training set chemicals. The results of molecular docking revealed that 10 active compounds had better docking scores with SRC and were linked to residues via hydrogen and hydrophobic bonds. Molecular dynamics was used to show the structural stability of obacunone, beta-sitosterol, and sitosterol.Conclusion:Pharmacophore 01 has high selectivity and the ability to distinguish active and inactive compounds, which is the optimal model for this study. Obacunone has the optimal binding ability with SRC. The pharmacophore model proposed in this study provides theoretical support for further screening effective anti-cancer Chinese herbal compounds and optimizing the compound structure.

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2D-QSAR model development and analysis on variant groups of anti -tuberculosis drugs

Cited by 16 publications

References 23 publications

2d-Qsar Study on Some Novel Dihydropyrimidine-4-Carbonitrile Analogs as an Antifungal Activity

2d-Qsar Study on Some Novel Dihydropyrimidine-4-Carbonitrile Analogs as an Antifungal Activity

In Silico Approaches to Identify Polyphenol Compounds as α-Glucosidase and α-Amylase Inhibitors against Type-II Diabetes

Study on the molecular mechanism of anti-liver cancer effect of Evodiae fructus by network pharmacology and QSAR model

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