MyMedR

QSAR study of substituted 1,3,4-oxadiazole naphthyridines as HIV-1 integrase inhibitors

Ravichandran V, Shalini S, Sundram K, Sokkalingam AD

Eur J Med Chem, 2010 Jul;45(7):2791-7.

PMID: 20347187 DOI: 10.1016/j.ejmech.2010.02.062

A linear quantitative structure activity relationship (QSAR) model is presented for modeling and predicting the inhibition of HIV-1 integrase. The model was produced by using the stepwise multiple linear regression technique on a database that consists of 67 recently discovered 1,3,4-oxadiazole substituted naphthyridine derivatives. The developed QSAR model was evaluated for statistical significance and predictive power. The key conclusion of this study is that valence connectivity index order 1, lowest unoccupied molecular orbital and dielectric energy significantly affect the inhibition of HIV-1 integrase activity by 1,3,4-oxadiazole substituted naphthyridine derivatives. The selected physicochemical descriptors serve as a first guideline for the design of novel and potent antagonists of HIV-1 integrase.

Matched MeSH terms: Quantitative Structure-Activity Relationship*

A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives

Algamal ZY, Lee MH

SAR QSAR Environ Res, 2017 Jan;28(1):75-90.

PMID: 28176549 DOI: 10.1080/1062936X.2017.1278618

A high-dimensional quantitative structure-activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.

Matched MeSH terms: Quantitative Structure-Activity Relationship*

A QSAR model for predicting antidiabetic activity of dipeptidyl peptidase-IV inhibitors by enhanced binary gravitational search algorithm

Al-Fakih AM, Algamal ZY, Lee MH, Aziz M, Ali HTM

SAR QSAR Environ Res, 2019 Jun;30(6):403-416.

PMID: 31122062 DOI: 10.1080/1062936X.2019.1607899

Time-varying binary gravitational search algorithm (TVBGSA) is proposed for predicting antidiabetic activity of 134 dipeptidyl peptidase-IV (DPP-IV) inhibitors. To improve the performance of the binary gravitational search algorithm (BGSA) method, we propose a dynamic time-varying transfer function. A new control parameter,
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, Y-randomization test, and applicability domain evaluation. The validation results indicate that the proposed TVBGSA model is robust and not due to chance correlation. The descriptor selection and prediction performance of TVBGSA outperform BGSA method. TVBGSA shows higher

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compared to obtained results by BGSA, indicating the best prediction performance of the proposed TVBGSA model. The results clearly reveal that the proposed TVBGSA method is useful for constructing reliable and robust QSARs for predicting antidiabetic activity of DPP-IV inhibitors prior to designing and experimental synthesizing of new DPP-IV inhibitors.

Matched MeSH terms: Quantitative Structure-Activity Relationship*

QSAR classification model for diverse series of antifungal agents based on improved binary differential search algorithm

Al-Fakih AM, Algamal ZY, Lee MH, Aziz M, Ali HTM

SAR QSAR Environ Res, 2019 Feb;30(2):131-143.

PMID: 30734580 DOI: 10.1080/1062936X.2019.1568298

An improved binary differential search (improved BDS) algorithm is proposed for QSAR classification of diverse series of antimicrobial compounds against Candida albicans inhibitors. The transfer functions is the most important component of the BDS algorithm, and converts continuous values of the donor into discrete values. In this paper, the eight types of transfer functions are investigated to verify their efficiency in improving BDS algorithm performance in QSAR classification. The performance was evaluated using three metrics: classification accuracy (CA), geometric mean of sensitivity and specificity (G-mean), and area under the curve. The Kruskal-Wallis test was also applied to show the statistical differences between the functions. Two functions, S1 and V4, show the best classification achievement, with a slightly better performance of V4 than S1. The V4 function takes the lowest iterations and selects the fewest descriptors. In addition, the V4 function yields the best CA and G-mean of 98.07% and 0.977%, respectively. The results prove that the V4 transfer function significantly improves the performance of the original BDS.

Matched MeSH terms: Quantitative Structure-Activity Relationship*

Study of the antimicrobial activity of cyclic cation-based ionic liquids via experimental and group contribution QSAR model

Ghanem OB, Shah SN, Lévêque JM, Mutalib MIA, El-Harbawi M, Khan AS, et al.

Chemosphere, 2018 Mar;195:21-28.

PMID: 29248749 DOI: 10.1016/j.chemosphere.2017.12.018

Over the past decades, Ionic liquids (ILs) have gained considerable attention from the scientific community in reason of their versatility and performance in many fields. However, they nowadays remain mainly for laboratory scale use. The main barrier hampering their use in a larger scale is their questionable ecological toxicity. This study investigated the effect of hydrophobic and hydrophilic cyclic cation-based ILs against four pathogenic bacteria that infect humans. For that, cations, either of aromatic character (imidazolium or pyridinium) or of non-aromatic nature, (pyrrolidinium or piperidinium), were selected with different alkyl chain lengths and combined with both hydrophilic and hydrophobic anionic moieties. The results clearly demonstrated that introducing of hydrophobic anion namely bis((trifluoromethyl)sulfonyl)amide, [NTF2] and the elongation of the cations substitutions dramatically affect ILs toxicity behaviour. The established toxicity data [50% effective concentration (EC50)] along with similar endpoint collected from previous work against Aeromonas hydrophila were combined to developed quantitative structure-activity relationship (QSAR) model for toxicity prediction. The model was developed and validated in the light of Organization for Economic Co-operation and Development (OECD) guidelines strategy, producing good correlation coefficient R2 of 0.904 and small mean square error (MSE) of 0.095. The reliability of the QSAR model was further determined using k-fold cross validation.

Matched MeSH terms: Quantitative Structure-Activity Relationship*

Predicting repurposed drugs targeting the NS3 protease of dengue virus using machine learning-based QSAR, molecular docking, and molecular dynamics simulations

Chongjun Y, Nasr AMS, Latif MAM, Rahman MBA, Marlisah E, Tejo BA

SAR QSAR Environ Res, 2024 Aug;35(8):707-728.

PMID: 39210743 DOI: 10.1080/1062936X.2024.2392677

Dengue fever, prevalent in Southeast Asian countries, currently lacks effective pharmaceutical interventions for virus replication control. This study employs a strategy that combines machine learning (ML)-based quantitative-structure-activity relationship (QSAR), molecular docking, and molecular dynamics simulations to discover potential inhibitors of the NS3 protease of the dengue virus. We used nine molecular fingerprints from PaDEL to extract features from the NS3 protease dataset of dengue virus type 2 in the ChEMBL database. Feature selection was achieved through the low variance threshold, F-Score, and recursive feature elimination (RFE) methods. Our investigation employed three ML models - support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) - for classifier development. Our SVM model, combined with SVM-RFE, had the best accuracy (0.866) and ROC_AUC (0.964) in the testing set. We identified potent inhibitors on the basis of the optimal classifier probabilities and docking binding affinities. SHAP and LIME analyses highlighted the significant molecular fingerprints (e.g. ExtFP69, ExtFP362, ExtFP576) involved in NS3 protease inhibitory activity. Molecular dynamics simulations indicated that amphotericin B exhibited the highest binding energy of -212 kJ/mol and formed a hydrogen bond with the critical residue Ser196. This approach enhances NS3 protease inhibitor identification and expedites the discovery of dengue therapeutics.

Matched MeSH terms: Quantitative Structure-Activity Relationship*

In vitro evaluation and in silico screening of synthetic acetylcholinesterase inhibitors bearing functionalized piperidine pharmacophores

Brahmachari G, Choo C, Ambure P, Roy K

Bioorg Med Chem, 2015 Aug 01;23(15):4567-4575.

PMID: 26105711 DOI: 10.1016/j.bmc.2015.06.005

A series of densely functionalized piperidine (FP) scaffolds was synthesized following a diastereoselective one-pot multicomponent protocol under eco-friendly conditions. The FPs were evaluated in vitro for their acetylcholinesterase (AChE) inhibitory activity, and in silico studies for all the target compounds were carried out using pharmacophore mapping, molecular docking and quantitative structure-activity relationship (QSAR) analysis in order to understand the structural features required for interaction with the AChE enzyme and the key active site residues involved in the intermolecular interactions. Halogenation, nitration or 3,4-methylenedixoxy-substitution at the phenyl ring attached to the 2- and 6-positions of 1,2,5,6-tetrahydropyridine nucleus in compounds 14-17, 19, 20, 24 and 26 greatly enhanced the AChE inhibitory activity. The docking analysis demonstrated that the inhibitors are well-fitted in the active sites. The in silico studies enlighten the future course of studies in modifying the scaffolds for better therapeutic efficacy against the deadly Alzheimer's disease.

Matched MeSH terms: Quantitative Structure-Activity Relationship

Theoretically-derived molecular descriptors important in human intestinal absorption

Agatonovic-Kustrin S, Beresford R, Yusof AP

J Pharm Biomed Anal, 2001 May;25(2):227-37.

PMID: 11275432

A quantitative structure-human intestinal absorption relationship was developed using artificial neural network (ANN) modeling. A set of 86 drug compounds and their experimentally-derived intestinal absorption values used in this study was gathered from the literature and a total of 57 global molecular descriptors, including constitutional, topological, chemical, geometrical and quantum chemical descriptors, calculated for each compound. A supervised network with radial basis transfer function was used to correlate calculated molecular descriptors with experimentally-derived measures of human intestinal absorption. A genetic algorithm was then used to select important molecular descriptors. Intestinal absorption values (IA%) were used as the ANN's output and calculated molecular descriptors as the inputs. The best genetic neural network (GNN) model with 15 input descriptors was chosen, and the significance of the selected descriptors for intestinal absorption examined. Results obtained with the model that was developed indicate that lipophilicity, conformational stability and inter-molecular interactions (polarity, and hydrogen bonding) have the largest impact on intestinal absorption.

Matched MeSH terms: Quantitative Structure-Activity Relationship

Fulltext QSAR Studies on Neuraminidase Inhibitors as Anti-influenza Agents

Veerasamy R, Rajak H

Turk J Pharm Sci, 2021 04 20;18(2):151-156.

PMID: 33900700 DOI: 10.4274/tjps.galenos.2020.45556

Objectives: The present study aimed to establish significant and validated quantitative structure-activity relationship (QSAR) models for neuraminidase inhibitors and correlate their physicochemical, steric, and electrostatic properties with their anti-influenza activity.

Materials and Methods: We have developed and validated 2D and 3D QSAR models by using multiple linear regression, partial least square regression, and k-nearest neighbor-molecular field analysis methods.

Results: 2D QSAR models had q2: 0.950 and pred_r2: 0.877 and 3D QSAR models had q2: 0.899 and pred_r2: 0.957. These results showed that the models werere predictive.

Conclusion: Parameters such as hydrogen count and hydrophilicity were involved in 2D QSAR models. The 3D QSAR study revealed that steric and hydrophobic descriptors were negatively contributed to neuraminidase inhibitory activity. The results of this study could be used as platform for design of better anti-influenza drugs.

Matched MeSH terms: Quantitative Structure-Activity Relationship

QSAR and Pharmacophore Mapping Studies on Benzothiazinimines to Relate their Structural Features with anti-HIV Activity

Geethaavacini G, Poh GP, Yan LY, Deepashini R, Shalini S, Harish R, et al.

Med Chem, 2018;14(7):733-740.

PMID: 29807521 DOI: 10.2174/1573406414666180529091618

BACKGROUND: The development of severe drug resistance caused by the extensive use of anti-HIV agents has resulted in a greatly extensive reduction in these drugs efficacy.

OBJECTIVES: To identify the important pharmacophoric features and correlate 3D chemical structure of benzothiazinimines with their anti-HIV potential using 2D, 3D-QSAR and pharmacophore modeling studies.

METHODS: QSAR and pharmacophore mapping studies have been used to relate structural features. 2D QSAR and 3D QSAR studies were performed using partial least square and k-nearest neighbor methodology, coupled with various feature selection methods, viz. stepwise, genetic algorithm, and simulated annealing, to derive QSAR models which were further validated for statistical significance.

RESULTS: The physicochemical descriptor XAHydrophilicArea and SsOHE-index, and alignmentindependent descriptor T_C_Cl_6 showed significant correlation with the anti-HIV activity of benzothiazinimines in 2D QSAR. 3D QSAR results showed the significant effect of electrostatic and steric field descriptors in the anti-HIV potential of benzothiazinimines. The generated pharmacophore hypothesis demonstrated the importance of aromaticity and hydrogen bond acceptors.

CONCLUSION: The significant models obtained in this study suggested that these techniques could be used as a guidance for designing new benzothiazinimines with enhanced anti-HIV potential.

Matched MeSH terms: Quantitative Structure-Activity Relationship

Fulltext Docking Based 3D-QSAR Study of Tricyclic Guanidine Analogues of Batzelladine K As Anti-Malarial Agents

Ahmed N, Anwar S, Thet Htar T

Front Chem, 2017;5:36.

PMID: 28664157 DOI: 10.3389/fchem.2017.00036

The Plasmodium falciparum Lactate Dehydrogenase enzyme (PfLDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The PfLDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of PfLDH, we have used Discovery studio to perform molecular docking in the active binding pocket of PfLDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q(2) of 0.516. The model has predicted r(2) of 0.91 showing that predicted IC50 values are in good agreement with experimental IC50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors.

Matched MeSH terms: Quantitative Structure-Activity Relationship

Models for skin and brain penetration of major components from essential oils used in aromatherapy for dementia patients

Agatonovic-Kustrin S, Chan CKY, Gegechkori V, Morton DW

J Biomol Struct Dyn, 2020 May;38(8):2402-2411.

PMID: 31204906 DOI: 10.1080/07391102.2019.1633408

Aromatherapy with essential oils (EOs) has been linked to improvement of cognitive function in patients with dementia. In order to act systemically, active EO components must be absorbed through the skin, enter the systemic circulation, and cross the blood brain barrier (BBB). Thus, the aim of this work was to develop quantitative structure activity relationships (QSARs), to predict skin and blood barrier penetrative abilities of 119 terpenoids from EOs used in aromatherapy. The first model was based on experimentally measured skin permeability for 162 molecules, and the second model on BBB permeability for 138 molecules. Each molecule was encoded with 63 calculated molecular descriptors and an artificial neural network was used to correlate molecular descriptors to permeabilities. Developed QSAR models confirm that EOs components penetrate through the skin and across the BBB. Some well-known descriptors, such as log P (lipophilicity), molecular size and shape, dominated the QSAR model for BBB permeability. Compounds with the highest predicted BBB penetration were hydrocarbon terpenes with the smallest molecular size and highest lipophilicity. Thus, molecular size is a limiting factor for penetration. Compounds with the highest skin permeability have slightly higher molecular size, high lipophilicity and low polarity. Our work shows that a major disadvantage of novel multitarget compounds developed for the treatment of Alzheimer's disease is the size of molecules, which cause problems in their delivery to the brain. Therefore, there is a need for smaller compounds, which possess more desirable physicochemical properties and pharmacokinetics, in addition to targeted biological effects.Communicated by Ramaswamy H. Sarma.

Matched MeSH terms: Quantitative Structure-Activity Relationship

TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers

Cao H, Ng MCK, Jusoh SA, Tai HK, Siu SWI

J Comput Aided Mol Des, 2017 Sep;31(9):855-865.

PMID: 28864946 DOI: 10.1007/s10822-017-0047-0

[Formula: see text]-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD <2.0 Å) and 78% of the cases are better predicted than the two other methods compared. Our method provides an alternative for modeling TM bitopic dimers of unknown structures for further computational studies. TMDIM is freely available on the web at https://cbbio.cis.umac.mo/TMDIM . Website is implemented in PHP, MySQL and Apache, with all major browsers supported.

Matched MeSH terms: Quantitative Structure-Activity Relationship

Prediction of Anti-Alzheimer's Activity of Flavonoids Targeting Acetylcholinesterase in silico

Das S, Laskar MA, Sarker SD, Choudhury MD, Choudhury PR, Mitra A, et al.

Phytochem Anal, 2017 Jul;28(4):324-331.

PMID: 28168765 DOI: 10.1002/pca.2679

INTRODUCTION: Prenylated and pyrano-flavonoids of the genus Artocarpus J. R. Forster & G. Forster are well known for their acetylcholinesterase (AChE) inhibitory, anti-cholinergic, anti-inflammatory, anti-microbial, anti-oxidant, anti-proliferative and tyrosinase inhibitory activities. Some of these compounds have also been shown to be effective against Alzheimer's disease.

OBJECTIVE: The aim of the in silico study was to establish protocols to predict the most effective flavonoid from prenylated and pyrano-flavonoid classes for AChE inhibition linking to the potential treatment of Alzheimer's disease.

METHODOLOGY: Three flavonoids isolated from Artocarpus anisophyllus Miq. were selected for the study. With these compounds, Lipinski filter, ADME/Tox screening, molecular docking and quantitative structure-activity relationship (QSAR) were performed in silico. In vitro activity was evaluated by bioactivity staining based on the Ellman's method.

RESULTS: In the Lipinski filter and ADME/Tox screening, all test compounds produced positive results, but in the target fishing, only one flavonoid could successfully target AChE. Molecular docking was performed on this flavonoid, and this compound gained the score as -13.5762. From the QSAR analysis the IC50 was found to be 1659.59 nM. Again, 100 derivatives were generated from the parent compound and docking was performed. The derivative compound 20 was the best scorer, i.e. -31.6392 and IC50 was predicted as 6.025 nM.

Matched MeSH terms: Quantitative Structure-Activity Relationship

Two dimensional-QSAR and molecular dynamics studies of a selected class of aldoxime- and hydroxy-functionalized chalcones as monoamine oxidase-B inhibitors

Mathew B, Ravichandran V, Raghuraman S, Rangarajan TM, Abdelgawad MA, Ahmad I, et al.

J Biomol Struct Dyn, 2023 Nov;41(19):9256-9266.

PMID: 36411738 DOI: 10.1080/07391102.2022.2146198

Candidates generated from unsaturated ketone (chalcone) demonstrated as strong, reversible and specific monoamine oxidase-B (MAO-B) inhibitory activity. For the research on MAO-B inhibition, our team has synthesized and evaluated a panel of aldoxime-chalcone ethers (ACE) and hydroxylchalcones (HC). The MAO-B inhibitory activity of several candidates is in the micro- to nanomolar range in these series. The purpose of this research was to develop predictive QSAR models and look into the relation between MAO-B inhibition by aldoxime and hydroxyl-functionalized chalcones. It was shown that the molecular descriptors ETA Shape P, MDEO-12, ETA dBetaP, SpMax1 Bhi and ETA EtaP B are significant in the inhibitory action of the MAO-B target. Using the current 2D QSAR models, potential chalcone-based MAO-B inhibitors might be created. The lead molecules were further analyzed by the detailed molecular dynamics study to establish the stability of the ligand-enzyme complex.Communicated by Ramaswamy H. Sarma.

Matched MeSH terms: Quantitative Structure-Activity Relationship

QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm

Algamal ZY, Qasim MK, Lee MH, Ali HTM

SAR QSAR Environ Res, 2020 Nov;31(11):803-814.

PMID: 32938208 DOI: 10.1080/1062936X.2020.1818616

High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSEtrain, Y-randomization test, MSEtest, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSEtrain. For the test dataset, proposed QSAR model shows lower value of MSEtest compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC50 values of neuraminidase inhibitors that have not been experimentally tested.

Matched MeSH terms: Quantitative Structure-Activity Relationship*

Synthesis of α, β-unsaturated carbonyl based compounds as acetylcholinesterase and butyrylcholinesterase inhibitors: characterization, molecular modeling, QSAR studies and effect against amyloid β-induced cytotoxicity

Bukhari SN, Jantan I, Masand VH, Mahajan DT, Sher M, Naeem-ul-Hassan M, et al.

Eur J Med Chem, 2014 Aug 18;83:355-65.

PMID: 24980117 DOI: 10.1016/j.ejmech.2014.06.034

A series of novel carbonyl compounds was synthesized by a simple, eco-friendly and efficient method. These compounds were screened for anti-oxidant activity, in vitro cytotoxicity and for inhibitory activity for acetylcholinesterase and butyrylcholinesterase. The effect of these compounds against amyloid β-induced cytotoxicity was also investigated. Among them, compound 14 exhibited strong free radical scavenging activity (18.39 μM) while six compounds (1, 3, 4, 13, 14, and 19) were found to be the most protective against Aβ-induced neuronal cell death in PC12 cells. Compounds 4 and 14, containing N-methyl-4-piperidone linker, showed high acetylcholinesterase inhibitory activity as compared to reference drug donepezil. Molecular docking and QSAR (Quantitative Structure-Activity Relationship) studies were also carried out to determine the structural features that are responsible for the acetylcholinesterase and butyrylcholinesterase inhibitory activity.

Matched MeSH terms: Quantitative Structure-Activity Relationship*

Fulltext Synthesis and sar study of diarylpentanoid analogues as new anti-inflammatory agents

Leong SW, Faudzi SM, Abas F, Aluwi MF, Rullah K, Wai LK, et al.

Molecules, 2014 Oct 09;19(10):16058-81.

PMID: 25302700 DOI: 10.3390/molecules191016058

A series of ninety-seven diarylpentanoid derivatives were synthesized and evaluated for their anti-inflammatory activity through NO suppression assay using interferone gamma (IFN-γ)/lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages. Twelve compounds (9, 25, 28, 43, 63, 64, 81, 83, 84, 86, 88 and 97) exhibited greater or similar NO inhibitory activity in comparison with curcumin (14.7 ± 0.2 µM), notably compounds 88 and 97, which demonstrated the most significant NO suppression activity with IC50 values of 4.9 ± 0.3 µM and 9.6 ± 0.5 µM, respectively. A structure-activity relationship (SAR) study revealed that the presence of a hydroxyl group in both aromatic rings is critical for bioactivity of these molecules. With the exception of the polyphenolic derivatives, low electron density in ring-A and high electron density in ring-B are important for enhancing NO inhibition. Meanwhile, pharmacophore mapping showed that hydroxyl substituents at both meta- and para-positions of ring-B could be the marker for highly active diarylpentanoid derivatives.

Matched MeSH terms: Quantitative Structure-Activity Relationship*

Fulltext Antioxidant activity of hispidin oligomers from medicinal fungi: a DFT study

El Hassane A, Shah SA, Hassan NB, El Moussaoui N, Ahmad R, Zulkefeli M, et al.

Molecules, 2014;19(3):3489-507.

PMID: 24662069 DOI: 10.3390/molecules19033489

Hispidin oligomers are styrylpyrone pigments isolated from the medicinal fungi Inonotus xeranticus and Phellinus linteus. They exhibit diverse biological activities and strong free radical scavenging activity. To rationalize the antioxidant activity of a series of four hispidin oligomers and determine the favored mechanism involved in free radical scavenging, DFT calculations were carried out at the B3P86/6-31+G (d, p) level of theory in gas and solvent. The results showed that bond dissociation enthalpies of OH groups of hispidin oligomers (ArOH) and spin density delocalization of related radicals (ArO•) are the appropriate parameters to clarify the differences between the observed antioxidant activities for the four oligomers. The effect of the number of hydroxyl groups and presence of a catechol moiety conjugated to a double bond on the antioxidant activity were determined. Thermodynamic and kinetic studies showed that the PC-ET mechanism is the main mechanism involved in free radical scavenging. The spin density distribution over phenoxyl radicals allows a better understanding of the hispidin oligomers formation.

Matched MeSH terms: Quantitative Structure-Activity Relationship

Discovery of new nanomolar peroxisome proliferator-activated receptor γ activators via elaborate ligand-based modeling

Al-Najjar BO, Wahab HA, Tengku Muhammad TS, Shu-Chien AC, Ahmad Noruddin NA, Taha MO

Eur J Med Chem, 2011 Jun;46(6):2513-29.

PMID: 21482446 DOI: 10.1016/j.ejmech.2011.03.040

Peroxisome Proliferator-Activated Receptor γ (PPARγ) activators have drawn great recent attention in the clinical management of type 2 diabetes mellitus, prompting several attempts to discover and optimize new PPARγ activators. With this in mind, we explored the pharmacophoric space of PPARγ using seven diverse sets of activators. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of accessing self-consistent and predictive quantitative structure-activity relationship (QSAR) (r2(71)=0.80, F=270.3, r2LOO=0.73, r2PRESS against 17 external test inhibitors=0.67). Three orthogonal pharmacophores emerged in the QSAR equation and were validated by receiver operating characteristic (ROC) curves analysis. The models were then used to screen the national cancer institute (NCI) list of compounds. The highest-ranking hits were tested in vitro. The most potent hits illustrated EC50 values of 15 and 224 nM.

Matched MeSH terms: Quantitative Structure-Activity Relationship

Displaying publications 21 - 40 of 62 in total