scholarly journals QSAR Analysis of Benzothiazole Derivatives of Antimalarial Compounds Based On AM1 Semi-Empirical Method

2015 ◽  
Vol 15 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Ruslin Hadanu ◽  
Salim Idris ◽  
I Wayan Sutapa
Keyword(s):  
Antimalarial Activity ◽  
Geometry Optimization ◽  
Theoretical Data ◽  
Qsar Model ◽  
Empirical Method ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Benzothiazole Derivatives ◽  
Semi Empirical ◽  
Antimalarial Compounds

Quantitative Structure and Activity Relationship (QSAR) analysis of 13 benzothiazoles derivatives compound as antimalarial compounds have been performed using electronic descriptor of the atomic net charges (q), dipole moment (μ), ELUMO, EHOMO and polarizability (α). The electronic structures as descriptors were calculated through HyperChem for Windows 7.0 using AM1 semi-empirical method. The descriptors were obtained through molecules modeling to get the most stable structure after geometry optimization step. The antimalarial activity (IC50) were taken from literature. The best model of QSAR model was determined by multiple linear regression approach and giving equation of QSAR: Log IC50 = 23.527 + 4.024 (qC4) + 273.416 (qC5) + 141.663 (qC6) – 0.567 (ELUMO) – 3.878 (EHOMO)– 2.096 (α). The equation was significant on the 95% level with statistical parameters: n = 13, r = 0.994, r2 = 0.987, SE = 0.094, Fcalc/Ftable = 11.212, and gave the PRESS = 0.348. Its means that there were only a relatively few deviations between the experimental and theoretical data of antimalarial activity.

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 QUANTITAVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS (QSAR) OF ANTIMALARIAL 1,10-PHENANTHROLINE DERIVATIVES COMPOUNDS

2010 ◽  
Vol 7 (1) ◽  
pp. 72-77 ◽  
Author(s):  
Ruslin Hadanu ◽  
Sabirin Mastjeh ◽  
Mustofa Mustofa ◽  
Eti Nurwening Sholikhah ◽  
Mahardika Agus Wijayanti ◽  
...  
Keyword(s):  
Antimalarial Activity ◽  
Theoretical Data ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Log P ◽  
Linear Regression Method ◽  
Statistical Parameters ◽  
Relationship Analysis ◽  
Structure Activity ◽  
Semi Empirical

Quantitative Electronic Structure-Activity Relationship (QSAR) analysis of a series of 1,10-phenanthroline derivatives as antiplasmodial compounds have been conducted using atomic net charges (q), dipole moment (μ) ELUMO, EHOMO, polarizability (α) and log P as the descriptors. The descriptors were obtained from computational chemistry method using semi-empirical PM3. Antiplasmodial activities were taken as the activity of the drugs  against  chloroquine-resistant Plasmodium falciparum FCR3 strain and are presented as the value of ln (1/IC50) where IC50 is an effective concentration inhibiting 50% of the parasite growth. The best model of QSAR model was determine by multiple linear regression method and giving equation of QSAR: ln 1/IC50  =  3.732 + (5.098) qC5 + (7.051) qC7 + (36.696) qC9 + (41.467) qC11 -(135.497) qC12 + (0.332) μ -                    (0.170) α + (0.757) log P. The equation was significant on the 95% level with statistical parameters: n=16; r=0.987; r2= 0.975; SE=0.317;  Fcalc/Ftable = 15.337 and gave the PRESS=0.707. Its means that there were only a relatively few deviations between the experimental and theoretical data of antimalarial activity.   Keywords: QSAR, antimalarial, semi-empirical method, 1,10-phenanthroline.

scholarly journals A QSAR Modeling on Aurone Derivatives as Antimalarial Agents

2020 ◽  
Vol 32 (11) ◽  
pp. 2839-2845
Author(s):  
R. Hadanau
Keyword(s):  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Multilinear Regression ◽  
Qsar Modeling ◽  
Qsar Analysis ◽  
Antimalarial Agents ◽  
Inhibition Concentration ◽  
Qsar Models ◽  
Semi Empirical ◽  
Statistical Criteria

A quantitative structure activity relationship (QSAR) analysis was performed on several compound and aurone derivatives (1-16) and 17-21 compounds were used as internal and external tests, respectively. Studies have investigated aurone derivatives; however, for aurone compounds, QSAR analysis has not been conducted. The semi-empirical PM3 method of HyperChem for Windows 8.0 was used to optimise the aurone derivative structures to acquire descriptors. For 15 influential descriptors, the multilinear regression MLR analysis was conducted by employing the backward method, and four new QSAR models were obtained. According to statistical criteria, model 2 was the optimum QSAR model for predicting the inhibition concentration (IC50) theoretical value against novel aurone derivatives. The modelling of 40 (22-61) aurone compounds was achieved. Six novel compounds (54, 55, 58, 59, 60, and 61) were synthesized in a laboratory because the IC50 of these compounds was lower than that of chloroquine (IC50 = 0.14 μM).

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).

scholarly journals Molecular Modelling, Docking and Pharmacokinetic Studies of N-Arylidenequinoline-3-Carbohydrazides Analogs as Novel β-Glucuronidase Inhibitors

2019 ◽  
Vol 64 (1) ◽  
Author(s):  
Muhammad Tukur Ibrahim ◽  
Adamu Uzairu ◽  
Abdullahi Bello Umar ◽  
Abubakar Sadiq Bello ◽  
Yusuf Isyaku
Keyword(s):  
Geometry Optimization ◽  
Docking Study ◽  
Empirical Method ◽  
Basis Set ◽  
Pharmacokinetic Studies ◽  
Amino Acid Residues ◽  
Molecular Docking Study ◽  
Data Set ◽  
New Compounds ◽  
Semi Empirical

Quantitative structure-activity relationships (QSAR) modelling on 30 N-Arylidenequinoline-3-carbohydrazides analogs was performed using Multi-Linear Regression (MLR) analysis adopting Genetic Function Algorithm (GFA) method. Semi empirical method using PM6 basis set was used for complete geometry optimization of the data set. The best model was chosen based on its statistical fit due to it good internal and external validations. From the Williams plot, it can be inferred that the reported model can make prediction of new compounds that are not within the data set. The molecular docking study showed that, the most active chemical in the data set was better than the standard β-glucuronidase inhibitor both in terms of binding scores and the amino acid residues that interacted with the drug and β-glucuronidase enzyme. The Pharmacokinetic studies indicated that none of the chemicals violated any of the condition set by the Lipinski′s Rule of five which confirm the bioavailability of these chemicals. The results these findings give room for designing novel β-glucuronidase inhibitors that are highly effective.                                                Resumen. Se llevó a cabo la técnica de QSAR en 30 analogos de N-arilidenequinolina-3-carbohidrazidas mediante el analisis de regresesión lineal múltiple (MLS) adopatando el método del algoritmo de función genética (GFA). Para la optimización completa de la geometría del conjunto de datos se utilizó un método semiémpirico del conjunto de bases PM6. El mejor modelo fue elegido basado en función de su ajuste estadístico debido a su validación interna y externa. A partir de la gráfica de Williams, se puede inferir que el modelo reportado puede predecir nuevos compuestos que no se encuentran en el conjunto de datos. Este estudio de acomplamiento molecular mostró que, el químico más activo del conjunto de datos fue mejor que el inhibidor estándar β-glucuronidasa, tanto en términos de unión y en términos de  interacción de los residuos con el fármaco y la enzima β-glucuronidasa. Los estudios farmacocinéticos que indicaron que ninguno de los fármacos incumple ninguna de las condiciones establecidas por la regla de cinco de Lipinski, en donde se confirma la biodisponibilidad de estos químicos. Los resultados de los hallazgos computacionales permiten diseñar nuevos inhibidores de la β-glucuronidasa que son altamente efectivos.

scholarly journals THEORETICAL STUDY OF THE EFFECT OF WATER MOLECULE ADDITION ON THE CONFORMATION OF SUBSTITUTED DIBENZO-18-CROWN-6 ETHER IN ITS COMPLEXATION WITH Na+ CATION USING SEMI EMPIRICAL METHOD MNDO/d

2010 ◽  
Vol 3 (2) ◽  
pp. 111-117
Author(s):  
Harno Dwi Pranowo ◽  
Tuti Hartati Siregar ◽  
Mudasir Mudasir
Keyword(s):  
Binding Energy ◽  
Water Molecule ◽  
Metal Ion ◽  
Geometry Optimization ◽  
Semiempirical Method ◽  
Empirical Method ◽  
Trial And Error ◽  
Effect Of Water ◽  
The Stability ◽  
Semi Empirical

The effect of water molecule addition into modeling structure of complex of substituted dibenzo-18-crown-6 ether with metal ion Na+ was studied. The aim of this research is to find information about geometrical conformation of substituted DB18C6 and its selectivity to complex/coordinate metal ion Na+ in the presence of water molecule. In this research semi empirical method was used for calculation. To find the best conformation, trial and error experiments were conducted using semi empirical method available in HyperChem 6.0, finally MNDO/d method was selected. The result of geometry optimization showed that addition of water molecule improve the stability of the conformation of substituted DB18C6 and increase the selectivity of this compound to complex metal ion Na+. The presence of electron-withdrawing substituents decreased the binding energy while that of electron-donating one increase the binding energy (value of DE more negative). Cavity radii of DB18C6 in the presence of water molecule extended from 2.3 Å to 2.6 Å. This figure is almost similar to that of experimental data.   Keywords: Crown ether, molecular modelling, semiempirical method

scholarly journals QSAR STUDY OF 1,10-PHENANTHROLINE DERIVATIVES AS THE ANTIMALARIAL COMPOUNDS USING ELECTRONIC DESCRIPTORS BASED ON SEMIEMPIRICAL AM1 CALCULATION

2010 ◽  
Vol 2 (2) ◽  
pp. 91-96
Author(s):  
Mustofa Mustofa ◽  
Iqmal Tahir ◽  
Jumina Jumina
Keyword(s):  
Antimalarial Drug ◽  
Quantitative Structure Activity Relationship ◽  
Antiplasmodial Activity ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Activity Data ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Net Charges ◽  
Semi Empirical

Quantitative Structure-Activity Relationship (QSAR) analysis of 1,10-phenantroline analogs as antimalarial drug has been conducted using atomic net charges (q) as predictors of their activity. Data of predictors are obtained from computational chemistry method using semi-empirical molecular orbital AM1 calculation. Antimalarial activities are taken as the activity of the drugs against plasmodium falciparum (FcM29-Cameroun) strain and are presented as the value of ln(1/IC50) where IC50 is an effective concentration inhibiting 50 % of the parasite growth.  The results show that there is correlation between antiplasmodial activity and electronic structure as represented by a linear function of activity versus atomic net charges of N1, C7, C10, C14 atoms on the 1,10-phenanthroline skeleton and is expressed by : log IC50 = -3,4398 - 14,9050 qN1 - 8,5589 qC10 - 14,7565 qC7 + 5,0457 qC11 The equation is significant at 95% level with statistical parameters : n = 13; r = 0,96275; r2 = 0,92689; SE = 0,61578 and F (4,8) = 25,3556.   Keywords: antimalarial drug; 1,10-phenanthroline; QSAR; antiplasmodial activity.

GA-SMLR-Based QSAR Modeling and Molecular Docking Studies of Bisamidine Derivatives as NMT Inhibitors

2020 ◽  
Vol 5 (4) ◽  
pp. 1-31
Author(s):  
Sapna Jain Dabade ◽  
Dheeraj Mandloi ◽  
Amritlal V. Bajaj ◽  
Naveen Dhingra
Keyword(s):  
Molecular Docking ◽  
External Validation ◽  
Qsar Model ◽  
Docking Studies ◽  
Qsar Modeling ◽  
Statistical Parameters ◽  
Van Der Waals Volume ◽  
Qsar Analysis ◽  
Hydrogen Bond Interactions

The present investigation deals with a combination of genetic algorithm-stepwise multiple linear regression (GA-SMLR)-based QSAR modeling and molecular docking applied to bisamidine analogues in an attempt to explore their role as novel NMT inhibitors of Candida albicans. In this regard, 43 bisamidine analogues were investigated for the development of mathematical models. The robustness of the proposed QSAR model was not only ascertained through traditionally used internal and external validation statistical parameters (Q2= 0.740, R2 = 0.819, R_Pred^2 = 0.636) but also through various R_(m)^2 metrics proposed by Roy and Mitra. The descriptors recognized in the QSAR analysis have culminated a significant role of atomic van der Waals volume, topology, nature of bond and dipole moment to modulate the antifungal activity of compounds under investigation. The most active compound revealed enhanced binding potency with MolDock score of -183.451 kcal/mol and displayed hydrogen bond interactions with active amino acids Leu177, Thr211, Tyr225, and IIe111 of NMT.

scholarly journals QUANTITATIVE ELECTRONIC STRUCTURE - ACTIVITY RELATIONSHIPS ANALYSIS ANTIMUTAGENIC BENZALACETONE DERIVATIVES BY PRINCIPAL COMPONENT REGRESSION APPROACH

2010 ◽  
Vol 4 (1) ◽  
pp. 68-75
Author(s):  
Yuliana Yuliana ◽  
Harno Dwi Pranowo ◽  
Jumina Jumina ◽  
Iqmal Tahir
Keyword(s):  
Electronic Structure ◽  
Latent Variables ◽  
Principal Component Regression ◽  
Theoretical Data ◽  
Principal Component ◽  
Antimutagenic Activity ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Net Charges

Quantitative Electronic Structure Activity Relationship (QSAR) analysis of a series of benzalacetones has been investigated based on semi empirical PM3 calculation data using Principal Components Regression (PCR). Investigation has been done based on antimutagen activity from benzalacetone compounds (presented by log 1/IC50) and was studied as linear correlation with latent variables (Tx) resulted from transformation of atomic net charges using Principal Component Analysis (PCA). QSAR equation was determinated based on distribution of selected components and then was analysed with PCR. The result was described by the following QSAR equation : log 1/IC50 = 6.555 + (2.177).T1 + (2.284).T2 + (1.933).T3 The equation was significant on the 95% level with statistical parameters : n = 28 r = 0.766  SE  = 0.245  Fcalculation/Ftable = 3.780 and gave the PRESS result 0.002. It means that there were only a relatively few deviations between the experimental and theoretical data of antimutagenic activity.          New types of benzalacetone derivative compounds were designed  and their theoretical activity were predicted based on the best QSAR equation. It was found that compounds number 29, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 44, 47, 48, 49 and 50  have  a relatively high antimutagenic activity.   Keywords: QSAR; antimutagenic activity; benzalaceton; atomic net charge

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