scholarly journals In-silico Design of Aryl and Aralkyl Amine-Based Triazolopyrimidine Derivatives with Enhanced Activity Against Resistant Plasmodium falciparum

2020 ◽  
Author(s):  
Zakari Ya’u Ibrahim ◽  
Adamu Uzairu ◽  
Gideon Shallangwa ◽  
Stephen Abechi
Keyword(s):  
In Silico ◽  
Density Functional ◽  
Antimalarial Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Randomization Test ◽  
Basis Set ◽  
Coefficient Of Determination ◽  
Statistical Parameters ◽  
Leave One Out

Abstract A blend of genetic algorithm with multiple linear regression (GA-MLR) method was utilized in generating a quantitative structure–activity relationship (QSAR) model on the antimalarial activity of aryl and aralkyl amine-based triazolopyrimidine derivatives. The structures of derivatives were optimized using density functional theory (DFT) DFT/B3LYP/6–31 + G* basis set to generate their molecular descriptors, where two (2) predictive models were developed with the aid of these descriptors. The model with an excellent statistical parameters; high coefficient of determination (R2) = 0.8884, cross-validated R2 (Q2cv) = 0.8317 and highest external validated R2 (R2pred) = 0.7019 was selected as the best model. The model generated was validated through internal (leave-one-out (LOO) cross-validation), external test set, and Y-randomization test. These parameters are indicators of robustness, excellent prediction, and validity of the selected model. The most relevant descriptor to the antimalarial activity in the model was found to be GATS6p (Geary autocorrelation—lag 6/weighted by polarizabilities), in the model due to its highest mean effect. The descriptor (GATS6p) was significant in the in-silico design of sixteen (16) derivatives of aryl and aralkyl amine-based triazolopyrimidine adopting compound DSM191 with the highest activity (pEC50 = 7.1805) as the design template. The design compound D8 was found to be the most active compound due to its superior hypothetical activity (pEC50 = 8.9545).

QSAR and Ab Initio studies of quinolon-4(1H)-imine derivatives as antimalarial agents

2021 ◽  
Vol 4 (1) ◽  
pp. 192
Author(s):  
Jafar La Kilo ◽  
Akram La Kilo ◽  
Saprini Hamdiani
Keyword(s):  
Antimalarial Activity ◽  
Molecular Orbital Calculation ◽  
Qsar Model ◽  
Basis Set ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships

Study on antimalarial activity of 22 quinolon-4(1H)-imine derivatives by using Quantitative Structure-Activity Relationships (QSAR) has been performed. Electronic and molecular descriptors were used in Quantitative Structure-Activity Relationships (QSAR) model and it was obtained from Hartree-Fock (HF) molecular orbital calculation with 6-31G basis set. QSAR analysis has been performed by multiple linear regression (MLR) method. The best equation of QSAR model on this study is: pEC50 = -4,177 + (37,902 x qC3) + (171,282 x qC8) + (9,061 x qC10) + (125,818 x qC11) + (-149,125 x qC17) + (191,623 x qC18), with statistical parameters, n = 22; r2 = 0,910; SEE = 0,171; Fcal/Ftab = 4,510 and PRESS = 0,697. The best equation can applied to design and predict new compounds with higher antimalarial activity.

scholarly journals Classical QSAR and Docking Simulation of 4-Pyridone Derivatives for Their Antimalarial Activity

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3166 ◽  
Author(s):  
Máryury Flores-Sumoza ◽  
Jackson Alcázar ◽  
Edgar Márquez ◽  
José Mora ◽  
Jesús Lezama ◽  
...  
Keyword(s):  
Density Functional ◽  
Cross Validation ◽  
Antimalarial Activity ◽  
Minimum Energy ◽  
Docking Simulation ◽  
Statistical Parameters ◽  
Functional Theory ◽  
Leave One Out ◽  
Small Chemical ◽  
Linear Regressions

In this work, the minimum energy structures of 22 4-pyridone derivatives have been optimized at Density Functional Theory level, and several quantum molecular, including electronic and thermodynamic descriptors, were computed for these substrates in order to obtain a statistical and meaningful QSAR equation. In this sense, by using multiple linear regressions, five mathematical models have been obtained. The best model with only four descriptors (r2 = 0.86, Q2 = 0.92, S.E.P = 0.38) was validated by the leave-one-out cross-validation method. The antimalarial activity can be explained by the combination of the four mentioned descriptors e.g., electronic potential, dipolar momentum, partition coefficient and molar refractivity. The statistical parameters of this model suggest that it is robust enough to predict the antimalarial activity of new possible compounds; consequently, three small chemical modifications into the structural core of these compounds were performed specifically on the most active compound of the series (compound 13). These three new suggested compounds were leveled as 13A, 13B and 13C, and the predicted biological antimalarial activity is 0.02 µM, 0.03 µM, and 0.07 µM, respectively. In order to complement these results focused on the possible action mechanism of the substrates, a docking simulation was included for these new structures as well as for the compound 13 and the docking scores (binding affinity) obtained for the interaction of these substrates with the cytochrome bc1, were −7.5, −7.2, −6.9 and −7.5 kcal/mol for 13A, 13B, 13C and compound 13, respectively, which suggests that these compounds are good candidates for its biological application in this illness.

scholarly journals Prediction of the Toxicity of Binary Mixtures by QSAR Approach Using the Hypothetical Descriptors

2018 ◽  
Vol 19 (11) ◽  
pp. 3423 ◽  
Author(s):  
Ting Wang ◽  
Lili Tang ◽  
Feng Luan ◽  
M. Natália D. S. Cordeiro
Keyword(s):  
Correlation Coefficient ◽  
Binary Mixtures ◽  
Quantitative Structure Activity Relationship ◽  
Training Set ◽  
Statistical Parameters ◽  
Test Set ◽  
Qsar Models ◽  
Forward Stepwise ◽  
Leave One Out ◽  
External Test

Organic compounds are often exposed to the environment, and have an adverse effect on the environment and human health in the form of mixtures, rather than as single chemicals. In this paper, we try to establish reliable and developed classical quantitative structure–activity relationship (QSAR) models to evaluate the toxicity of 99 binary mixtures. The derived QSAR models were built by forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNNs) using the hypothetical descriptors, respectively. The statistical parameters of the MLR model provided were N (number of compounds in training set) = 79, R2 (the correlation coefficient between the predicted and observed activities)= 0.869, LOOq2 (leave-one-out correlation coefficient) = 0.864, F (Fisher’s test) = 165.494, and RMS (root mean square) = 0.599 for the training set, and Next (number of compounds in external test set) = 20, R2 = 0.853, qext2 (leave-one-out correlation coefficient for test set)= 0.825, F = 30.861, and RMS = 0.691 for the external test set. The RBFNN model gave the statistical results, namely N = 79, R2 = 0.925, LOOq2 = 0.924, F = 950.686, RMS = 0.447 for the training set, and Next = 20, R2 = 0.896, qext2 = 0.890, F = 155.424, RMS = 0.547 for the external test set. Both of the MLR and RBFNN models were evaluated by some statistical parameters and methods. The results confirm that the built models are acceptable, and can be used to predict the toxicity of the binary mixtures.

scholarly journals A chemometric approach for prediction of antifungal activity of some benzoxazole derivatives against Candida albicans

2012 ◽  
pp. 273-282 ◽  
Author(s):  
Sanja Podunavac-Kuzmanovic ◽  
Lidija Jevric ◽  
Strahinja Kovacevic ◽  
Natasa Kalajdzija
Keyword(s):  
Candida Albicans ◽  
Antifungal Activity ◽  
Clinical Importance ◽  
Quantitative Structure Activity Relationship ◽  
Statistical Parameters ◽  
Relationship Analysis ◽  
Calibration Models ◽  
Chemometric Approach ◽  
Leave One Out

The purpose of the article is to promote and facilitate prediction of antifungal activity of the investigated series of benzoxazoles against Candida albicans. The clinical importance of this investigation is to simplify design of new antifungal agents against the fungi which can cause serious illnesses in humans. Quantitative structure activity relationship analysis was applied on nineteen benzoxazole derivatives. A multiple linear regression (MLR) procedure was used to model the relationships between the molecular descriptors and the antifungal activity of benzoxazole derivatives. Two mathematical models have been developed as a calibration models for predicting the inhibitory activity of this class of compounds against Candida albicans. The quality of the models was validated by the leave-one-out technique, as well as by the calculation of statistical parameters for the established model.

In vitro evaluation and molecular dynamics, DFT guided investigation of antimalarial activity of ethnomedicinally used Coptis teeta Wall

2021 ◽  
Vol 24 ◽  
Author(s):  
Ashis Kumar Goswami ◽  
Hemanta Kumar Sharma ◽  
Neelutpal Gogoi ◽  
Ankita Kashyap ◽  
Bhaskar Jyoti Gogoi
Keyword(s):  
Molecular Dynamics ◽  
In Silico ◽  
Density Functional ◽  
Antimalarial Activity ◽  
In Silico Analysis ◽  
Dynamics Simulation ◽  
Discovery Studio ◽  
North East ◽  
Silico Analysis

Background: Malaria is caused by different species of Plasmodium; among which P. falciparum is the most severe. Coptis teeta is an ethnomedicinal plant of enormous importance for tribes of north east India. Objective: In this study, the anti malarial activity of the methanol extracts of Coptis teeta was evaluated in vitro and lead identification via in silico study. Method: On the basis of the in vitro results, in silico analysis by application of different modules of Discovery Studio 2018 was performed on multiple targets of P. falciparum taking into consideration some of the compounds reported from C. teeta. Results: The IC50 of the methanol extract of Coptis teeta 0.08 µg/ml in 3D7 strain and 0.7 µg/ml in Dd2 strain of P. falciparum. From the docking study, noroxyhydrastatine was observed to have better binding affinity in comparison to chloroquine. The binding of noroxyhydrastinine with dihydroorotate dehydrogenase was further validated by molecular dynamics simulation and was observed to be significantly stable in comparison to the co-crystal inhibitor. During simulations it was observed that noroxyhydrastinine retained the interactions, giving strong indications of its effectiveness against the P. falciparum proteins and stability in the binding pocket. From the Density-functional theory analysis, the band gap energy of noroxyhydrastinine was found to be 0.186 Ha indicating a favourable interaction. Conclusion: The in silico analysis as an addition to the in vitro results provide strong evidence of noroxyhydrastinine as an anti malarial agent.

scholarly journals ADME/Tox Properties and Biochemical Interactions of Silybin Congeners: In silico Study

2017 ◽  
Vol 12 (2) ◽  
pp. 1934578X1701200 ◽  
Author(s):  
Antonia Diukendjieva ◽  
Merilin Al Sharif ◽  
Petko Alov ◽  
Tania Pencheva ◽  
Ivanka Tsakovska ◽  
...  
Keyword(s):  
In Silico ◽  
Estrogen Receptor Alpha ◽  
Estrogenic Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Toxic Effects ◽  
Active Constituent ◽  
Main Component

Silymarin, the active constituent of Silybum marianum (milk thistle), and its main component, silybin, are products with well-known hepatoprotective, cytoprotective, antioxidant, and chemopreventative properties. Despite substantial in vitro and in vivo investigations of these flavonolignans, their mechanisms of action and potential toxic effects are not fully defined. In this study we explored important ADME/Tox properties and biochemical interactions of selected flavonolignans using in silico methods. A quantitative structure–activity relationship (QSAR) model based on data from a parallel artificial membrane permeability assay (PAMPA) was used to estimate bioavailability after oral administration. Toxic effects and metabolic transformations were predicted using the knowledge-based expert systems Derek Nexus and Meteor Nexus (Lhasa Ltd). Potential estrogenic activity of the studied silybin congeners was outlined. To address further the stereospecificity of this effect the stereoisomeric forms of silybin were docked into the ligand-binding domain of the human estrogen receptor alpha (ERα) (MOE software, CCG). According to our results both stereoisomers can be accommodated into the ERα active site, but different poses and interactions were observed for silybin A and silybin B.

scholarly journals 2D-QSAR Study of Indolylpyrimidines Derivative as Antibacterial against Pseudomonas aeruginosa and Staphylococcus aureus: A Comparative Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Prasanna A. Datar
Keyword(s):  
Staphylococcus Aureus ◽  
Pseudomonas Aeruginosa ◽  
Qsar Model ◽  
Antibacterial Activities ◽  
Quantitative Structure Activity Relationship ◽  
Comparative Approach ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Leave One Out ◽  
And Staphylococcus Aureus

A set of 15 indolylpyrimidine derivatives with their antibacterial activities in terms of minimum inhibitory concentration against the gram-negative bacteria Pseudomonas aeruginosa and gram-positive Staphylococcus aureus were selected for 2D quantitative structure activity relationship (QSAR) analysis. QSAR was performed using a combination of various descriptors such as steric, electronic and topological. Stepwise regression method was used to derive the most significant QSAR equation for predicting the inhibitory activity of this class of molecules. The best QSAR model was further validated by a leave one out technique as well as by the random trials. A high correlation between experimental and predicted inhibitory values was observed. A comparative picture of behavior of indolylpyrimidines against both of the microorganisms is discussed.

scholarly journals In-silico Molecular Docking and ADME/Pharmacokinetic Prediction Studies of Some Novel Carboxamide Derivatives as Anti-tubercular Agents

2020 ◽  
Vol 3 (4) ◽  
pp. 989-1000
Author(s):  
Mustapha Abdullahi ◽  
Shola Elijah Adeniji
Keyword(s):  
Molecular Docking ◽  
Binding Affinity ◽  
In Silico ◽  
Density Functional ◽  
Docking Simulation ◽  
Basis Set ◽  
Hybrid Functional ◽  
Standard Drug

AbstractMolecular docking simulation of thirty-five (35) molecules of N-(2-phenoxy)ethyl imidazo[1,2-a]pyridine-3-carboxamide (IPA) with Mycobacterium tuberculosis target (DNA gyrase) was carried out so as to evaluate their theoretical binding affinities. The chemical structure of the molecules was accurately drawn using ChemDraw Ultra software, then optimized at density functional theory (DFT) using Becke’s three-parameter Lee–Yang–Parr hybrid functional (B3LYP/6-311**) basis set in a vacuum of Spartan 14 software. Subsequently, the docking operation was carried out using PyRx virtual screening software. Molecule 35 (M35) with the highest binding affinity of − 7.2 kcal/mol was selected as the lead molecule for structural modification which led to the development of four (4) newly hypothetical molecules D1, D2, D3 and D4. In addition, the D4 molecule with the highest binding affinity value of − 9.4 kcal/mol formed more H-bond interactions signifying better orientation of the ligand in the binding site compared to M35 and isoniazid standard drug. In-silico ADME and drug-likeness prediction of the molecules showed good pharmacokinetic properties having high gastrointestinal absorption, orally bioavailable, and less toxic. The outcome of the present research strengthens the relevance of these compounds as promising lead candidates for the treatment of multidrug-resistant tuberculosis which could help the medicinal chemists and pharmaceutical professionals in further designing and synthesis of more potent drug candidates. Moreover, the research also encouraged the in vivo and in vitro evaluation study for the proposed designed compounds to validate the computational findings.

scholarly journals QSAR Analysis of 5-Substituted-2-Benzoyl-aminobenzoic acids as PPAR Modulator

2004 ◽  
Vol 1 (5) ◽  
pp. 243-250 ◽  
Author(s):  
R. Hemalatha ◽  
L. K. Soni ◽  
A. K. Gupta ◽  
S. G. Kaskhedikar
Keyword(s):  
Linear Regression Analysis ◽  
Qsar Model ◽  
Multiple Linear Regression Analysis ◽  
Quantitative Structure Activity Relationship ◽  
Statistical Regression ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Aminobenzoic Acids ◽  
Leave One Out ◽  
Α Activity

A quantitative structure activity relationship (QSAR) study on a series of analogs of 5-aryl thiazolidine-2, 4-diones with activity on PPAR-α and PPAR-γwas made using combination of various thermodynamic, electronic and spatial descriptors. Several statistical regression expressions were obtained using multiple linear regression analysis. The best QSAR model was further validated by leave one out cross validation method. The studied revealed that for dual PPAR-α/γactivity dipole-dipole energy and PMI-Z play significant role and contributed positively for PPAR-γand PPAR-α activity respectively. Thus, QSAR brings important structural insight to aid the design of dual PPAR-α /γreceptor agonist.

scholarly journals QSAR modeling of antibacterial activity of some benzimidazole derivatives

2011 ◽  
Vol 17 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Sanja Podunavac-Kuzmanovic ◽  
Dragoljub Cvetkovic
Keyword(s):  
Antibacterial Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Benzimidazole Derivatives ◽  
Qsar Modeling ◽  
Qsar Study ◽  
Validation Procedure ◽  
Leave One Out

A quantitative structure-activity relationship (QSAR) study has been carried out for training set of 12 benzimidazole derivatives to correlate and predict the antibacterial activity of studied compounds against Gram-negative bacteria Pseudomonas aeruginosa. Multiple linear regression was used to select the descriptors and to generate the best prediction model that relates the structural features to inhibitory activity. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based on the following descriptors: parameter of lipophilicity (logP) and hydration energy (HE). Good agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the generated QSAR model.

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