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

scholarly journals QUANTITATIVE ELECTRONIC STRUCTURE - ACTIVITY RELATIONSHIP OF ANTIMALARIAL COMPOUND OF ARTEMISININ DERIVATIVES USING PRINCIPAL COMPONENT REGRESSION APPROACH

2010 ◽  
Vol 8 (3) ◽  
pp. 404-410
Author(s):  
Paul Robert Martin Werfette ◽  
Ria Armunanto ◽  
Iqmal Tahir
Keyword(s):  
Electronic Structure ◽  
Latent Variables ◽  
Principal Component Regression ◽  
Principal Component ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Data Set ◽  
Artemisinin Derivatives ◽  
Structure Activity ◽  
Net Charges

Analysis of quantitative structure - activity relationship (QSAR) for a series of antimalarial compound artemisinin derivatives has been done using principal component regression. The descriptors for QSAR study were representation of electronic structure i.e. atomic net charges of the artemisinin skeleton calculated by AM1 semi-empirical method. The antimalarial activity of the compound was expressed in log 1/IC50 which is an experimental data. The main purpose of the principal component analysis approach is to transform a large data set of atomic net charges to simplify into a data set which known as latent variables. The best QSAR equation to analyze of log 1/IC50 can be obtained from the regression method as a linear function of several latent variables i.e. x1, x2, x3, x4 and x5. The best QSAR model is expressed in the following equation,  (;;)   Keywords: QSAR, antimalarial, artemisinin, principal component regression

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.

Structural requirements of salicylaldehyde benzoylhydrazones and their Cu(II) complexes for anticancer activity

2012 ◽  
Vol 90 (9) ◽  
pp. 762-775 ◽  
Author(s):  
Shiow Jin Tan ◽  
Mahasin Alam Sk ◽  
Peter Peng Foo Lee ◽  
Yaw Kai Yan ◽  
Kok Hwa Lim
Keyword(s):  
Anticancer Activity ◽  
Density Functional ◽  
Chemical Potential ◽  
Biological Activities ◽  
Principal Component Regression ◽  
Principal Component ◽  
Atomic Charges ◽  
Anticancer Activities ◽  
Structural Requirements ◽  
Qsar Analysis

Salicylaldehyde benzoylhydrazone (H2sb) has a variety of biological activities including anticancer activity. The Cu(II) complexes of H2sbs possess enhanced anticancer activity as compared with their free ligands. A quantitative structure–activity relationship (QSAR) analysis was performed on a series of H2sb ligands and their corresponding Cu(II) complexes to capture the structural requirements that are responsible for the bioactivity. The predictive QSAR models were developed using statistical techniques such as multiple linear regression (MLR) and principal component regression analysis (PCRA). We used different combinations of various descriptors such as a physicochemical descriptor, electrotopological state atom (ETSA) indices, and descriptors derived from density functional theory (DFT) calculations. The DFT-derived descriptors used for QSAR analysis are HOMO and LUMO energies, atomic charges, chemical potential, and hardness. Our developed models showed the importance of the lipophilicity index (ClogP), ETSA indices, and atomic charges for anticancer activities of the H2sb analogs and their Cu(II) complexes. In addition, our MLR models revealed that, while the global lipophilicity index and hardness are important for anticancer activity of H2sb ligands, chemical potential and HOMO energy are important for the anticancer activity of Cu(II) complexes.

A Calibration Tutorial for Spectral Data. Part 1: Data Pretreatment and Principal Component Regression Using Matlab

1996 ◽  
Vol 4 (1) ◽  
pp. 225-242 ◽  
Author(s):  
Paul Geladi ◽  
Harald Martens
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Regression Models ◽  
Principal Component Regression ◽  
Principal Component ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Statistical Parameters ◽  
Calibration Samples

Regression and calibration play an important role in analytical chemistry. All analytical instrumentation is dependent on a calibration that uses some regression model for a set of calibration samples. The ordinary least squares (OLS) method of building a multivariate linear regression (MLR) model has strict limitations. Therefore, biased or regularised regression models have been introduced. Some selected ones are ridge regression (RR), principal component regression (PCR) and partial least squares regression (PLS or PLSR). Also, artificial neural networks (ANN) based on back-propagation can be used as regression models. In order to understand regression models more is needed than just a set of statistical parameters. A deeper understanding of the underlying chemistry and physics is always equally important. For spectral data this means that a basic understanding of spectra and their errors is useful and that spectral representation should be included in judging the usefulness of the data treatment. A “constructed” spectrometric example is introduced. It consists of real spectrometric measurements in the range 408–1176 nm for 26 calibration samples and 10 test samples. The main response variable is litmus concentration, but other constituents such as bromocresolgreen and ZnO are added as interferents and also the pH is changed. The example is introduced as a tutorial. All calculations are shown in detail in Matlab. This makes it easy for the reader to follow and understand the calculations. It also makes the calculations completely traceable. The raw data are available as a file. In Part 1, the emphasis is on pretreatment of the data and on visualisation in different stages of the calculations. Part 1 ends with principal component regression calculations. Partial least squares calculations and some ANN results are presented in Part 2.

scholarly journals Structure Features of Peptide-Type SARS-CoV Main Protease Inhibitors: Quantitative Structure Activity Relationship Study

2020 ◽  
Author(s):  
Vijay Masand ◽  
Ajaykumar Gandhi ◽  
Vesna Rastija ◽  
Meghshyam K. Patil
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Atom Pairs ◽  
Main Protease ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships

<div>In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80–0.82, Q2loo = 0.74–0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.</div><div><br></div>

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.

scholarly journals Chemometric and QSAR analysis of some thiadiazines as potential antifungal agents

2017 ◽  
pp. 117-126
Author(s):  
Milica Karadzic ◽  
Strahinja Kovacevic ◽  
Lidija Jevric ◽  
Sanja Podunavac-Kuzmanovic
Keyword(s):  
Antifungal Activity ◽  
Antifungal Agents ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Linear Modeling ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Qsar Models

Quantitative structure-activity relationship (QSAR) analysis has been performed in order to predict the antifungal activity of dihydroindeno and indeno thiadiazines against toxigenic fungus Aspergillus flavus. The studied compounds were classified according to their lipophilicity using the principal component analysis (PCA). The partial least square regression (PLSR) was used to distinguish the most important molecular descriptors for non-linear modeling. Artificial neural networks (ANNs) were applied for the antifungal activity prediction. The best QSAR models were validated by statistical parameters and graphical methods. High agreement between the observed and predicted antifungal activity values indicated the good quality of the derived QSAR models. The obtained QSAR-ANN models can be used to predict the antifungal activity of dihydroindeno and indeno thiadiazines and of structurally similar compounds. The modeling of the antifungal activity can contribute to the synthesis of new antifungal agents with better ability to protect food and feed from the mycotoxins.

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 Effect of Genetic Algorithm–Based Wavelength Selection as a Preprocessing Tool on Multivariate Simultaneous Determination of Paracetamol, Orphenadrine Citrate, and Caffeine in the Presence of p-Aminophenol Impurity

2020 ◽  
Vol 103 (1) ◽  
pp. 250-256 ◽  
Author(s):  
Shereen A Boltia ◽  
Aya T Soudi ◽  
Eman S Elzanfaly ◽  
Hala E Zaazaa
Keyword(s):  
Genetic Algorithm ◽  
Simultaneous Determination ◽  
Latent Variables ◽  
Principal Component Regression ◽  
Principal Component ◽  
Standard Addition ◽  
Pharmaceutical Formulations ◽  
Wavelength Selection ◽  
Full Spectrum

Abstract Background: The utilization of selection methods such as genetic algorithm (GA) aims to construct better partial least squares (PLS) and principal component regression (PCR) models than those established from the full-spectrum range. Objective: Determination of paracetamol (PAR), orphenadrine citrate (Or.cit), and caffeine (CAF) in the presence of PAR nephrotoxic impurity [p-aminophenol (PAP)]. GA was applied to select the optimum wavelengths used. Methods: A calibration set was prepared in which the three drugs, together with PAP, were modeled by multilevel multifactor design. This calibration set was used to build the PLS and PCR models, either with or without preprocessing the data using GA. Results: Results were compared with and without preprocessing, and this revealed that GA can find an optimized combination of spectral wavelengths, yielding a lower root mean square error of prediction as well as a lower number of latent variables used. The results of the two models show that simultaneous determination of the aforementioned drugs can be performed in the concentration ranges of 20–60, 3–11, and 1–9 μg/mL for PAR, Or.cit, and CAF, respectively. Conclusions: The proposed models were applied for the determination of the three drugs in their pharmaceutical formulations, and the results were verified by the standard addition technique. Highlights: GA can be useful as a wavelength selection tool before applying multivariate PLS and PCR methods. GA gives an improvement in the predictive ability of the models with lower RMSEP and less number of latent variables (LVs). The proposed PLS, PCR, GA-PLS, and GA-PCR spectrophotometric methods were able to determine paracetamol, orphenadrine citrate, and caffeine in the presence of p-aminophenol and severe spectral overlapping.

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