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 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 Structure-Activity Relationship Analysis of Xanthone Derivates as Cytotoxic Agents in Liver Cancer Cell Line HepG2

Molekul ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. 143
Author(s):  
Isnatin Miladiyah ◽  
Iqmal Tahir ◽  
Jumina Jumina ◽  
Sofia Mubarika ◽  
Mustofa Mustofa
Keyword(s):  
Latent Variables ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Cytotoxic Agents ◽  
Activity Relationship ◽  
Log P ◽  
Quantitative Structure ◽  
Data Set ◽  
Relationship Analysis ◽  
Structure Activity

The study of xanthone derivatives as cytotoxic agents in cancer is increasing. This study was conducted to explore the Quantitative Structure-Activity Relationship (QSAR) of xanthones as cytotoxic agents in HepG2 cells, to find compounds with better potency. The data set were taken from the previous study, involving 26 xanthone derivates and their cytotoxic activities in Inhibitory Concentration 50% (IC50). The parameters (descriptors) were obtained from quantum mechanics calculation using semiempirical AM1 method and QSAR models determined with principle component regression, with log (1/IC50) as a dependent variable and five latent variables as independent variables. From the 26 main descriptors, PCR reduced them to five latent variables (1st– 5th LV). The QSAR analysis gave the best model as follows: log (1/IC50) = 4.592 – 0.204 LV1 + 0.295 LV2 + 0.028 LV3 (n = 26, r = 0.571, SE = 0.234, Fcount/Ftable ratio = 1.165, PRESS value = 3.766). The study concluded that the descriptors contributed to anticancer activity were volume, mass, surface area, log P, dipole moment, HOMO energy, LUMO energy, and atomic net charge of some atoms. Modifications of substitution that would contribute to cytotoxic activity can be performed at phenyl ring A and C, but not at B.

Prediction of Lipophilicity of some Quinolone Derivatives by using Quantitative Structure- Activity Relationship

2019 ◽  
Vol 16 ◽  
Author(s):  
Meysam Shirmohammadi ◽  
Zakiyeh Bayat ◽  
Esmat Mohammadinasab
Keyword(s):  
Partition Coefficient ◽  
Principal Component ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Selection Methods ◽  
Qsar Study ◽  
Structure Activity ◽  
Descriptor Selection

: Quantitative structure activity relationship (QSAR) was used to study the partition coefficient of some quinolones and their derivatives. These molecules are broad-spectrum antibiotic pharmaceutics. First, data were divided into two categories of train and test (validation) sets using random selection method. Second, three approaches including stepwise selection (STS) (forward), genetic algorithm (GA), and simulated annealing (SA) were used to select the descriptors, with the aim of examining the effect feature selection methods. To find the relation between descriptors and partition coefficient, multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) were used. QSAR study showed that the both regression and descriptor selection methods have vital role in the results. Different statistical metrics showed that the MLR-SA approach with (r2=0.96, q2=0.91, pred_r2=0.95) gives the best outcome. The proposed expression by MLR-SA approach can be used in the better design of novel quinolones and their derivatives.

scholarly journals A Computational Approach to Drug Discovery: Search for Chalcone Analogues as the Potential Candidates for Anti Colorectal Cancer (HT29)

2018 ◽  
Vol 17 (2) ◽  
pp. 64-74
Author(s):  
Neni FRIMAYANTI ◽  
Ihsan IKHTIARUDIN ◽  
Rahma DONA ◽  
Tiara Tri AGUSTINI ◽  
Fri MURDIYA ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Molecular Dynamic ◽  
Predictive Ability ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Data Set ◽  
Structure Activity

A series of 46 chalcone derivative compounds with their inhibitory activity against colorectal cancer were used as data set for developing the quantitative structure activity relationship (QSAR). 2D QSAR and 3D QSAR models have been developed with high predictive ability with r2 and r2(CV) of 0.81 and 0.78, respectively. Results from the 2D and 3D quantitative structure activity relationship models indicate that electrostatic parameter enhanced bioactivity of the chalcone derivatives. Further, docking and molecular dynamic simulation was performed using 2wft PDB ID as the molecular target of colon cancer. Based on the docking, molecular dynamic, and biological assay, it is confirmed that compound 2, cpd 4, cpd 21, cpd 23, cpd 27, cpd 32, cpd 38, and cpd 39 show better activity (active) against colorectal cancer cells.

scholarly journals Efficiency and Quantitative Structure-Activity Relationship of Monoaromatics Oxidation by Quinone-Activated Persulfate

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiaqi Shi ◽  
Tao Long ◽  
Yuxuan Zhou ◽  
Lei Wang ◽  
Cuiping Jiang ◽  
...  
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Persulfate Oxidation ◽  
Alkaline Environment ◽  
Structure Activity ◽  
Degradation Rates ◽  
Relationship Model ◽  
Net Charges

Quinones and quinone-containing organics have potential of activating persulfate to produce sulfate radical. In this work, the optimal condition for quinone activation of persulfate was investigated. It was found representative monoaromatics were degraded fastest in alkaline environment (pH 10.0), but excessive alkalinity restrained the reaction instead. The mechanisms to explain this phenomenon were speculated. The effect of initial quinone concentration on persulfate oxidation was also investigated at pH 10.0. In addition, a quantitative structure-activity relationship model was established with 15 kinds of monoaromatics, which revealed the most negative atomic net charges on carbon atom played an important role on degradation rates. Chemicals with a smaller qC- were easier oxidized in quinone-activate system. This finding helps further exploration of effective activator in alkaline environment.

scholarly journals Complementary Quantitative Structure–Activity Relationship Models for the Antitrypanosomal Activity of Sesquiterpene Lactones

2018 ◽  
Vol 19 (12) ◽  
pp. 3721 ◽  
Author(s):  
Njogu M. Kimani ◽  
Josphat C. Matasyoh ◽  
Marcel Kaiser ◽  
Mauro S. Nogueira ◽  
Gustavo H. G. Trossini ◽  
...  
Keyword(s):  
Sesquiterpene Lactones ◽  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Data Set ◽  
Structure Activity ◽  
Antitrypanosomal Activity

Three complementary quantitative structure–activity relationship (QSAR) methodologies, namely, regression modeling based on (i) “classical” molecular descriptors, (ii) 3D pharmacophore features, and (iii) 2D molecular holograms (HQSAR) were employed on the antitrypanosomal activity of sesquiterpene lactones (STLs) toward Trypanosoma brucei rhodesiense (Tbr), the causative agent of the East African form of human African trypanosomiasis. In this study, an extension of a previous QSAR study on 69 STLs, models for a much larger and more diverse set of such natural products, now comprising 130 STLs of various structural subclasses, were established. The extended data set comprises a variety of STLs isolated and tested for antitrypanosomal activity within our group and is furthermore enhanced by 12 compounds obtained from literature, which have been tested in the same laboratory under identical conditions. Detailed QSAR analyses yielded models with comparable and good internal and external predictive ability. For a set of compounds as chemically diverse as the one under study, the models exhibited good coefficients of determination (R2) ranging from 0.71 to 0.85, as well as internal (leave-one-out Q2 values ranging from 0.62 to 0.72) and external validation coefficients (P2 values ranging from 0.54 to 0.73). The contributions of the various tested descriptors to the generated models are in good agreement with the results of previous QSAR studies and corroborate the fact that the antitrypanosomal activity of STLs is very much dependent on the presence and relative position of reactive enone groups within the molecular structure but is influenced by their hydrophilic/hydrophobic properties and molecular shape.

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