scholarly journals RELATION OF ELECTRONIC STRUCTURES WITH THEIR ANTIMALARIAL ACTIVITIES ON ARTEMISININ DERIVATIVES

2010 ◽  
Vol 4 (3) ◽  
pp. 212-217 ◽  
Author(s):  
Ria Armunanto ◽  
Sri Sudiono
Keyword(s):  
Electronic Structures ◽  
Antimalarial Activity ◽  
Atomic Charge ◽  
Quantitative Structure Activity Relationship ◽  
Equation Model ◽  
Atomic Charges ◽  
Quantitative Structure ◽  
Artemisinin Derivatives ◽  
Structure Activity

Relation of electronic structures with their anti malaria activities on artemisinin derivatives was evaluated by means of quantitative structure activity relationship (QSAR) method. To describe electronic structures, atomic charges and dipol moments calculated by quantum mechanics on PM3 semiempirical level. A linear relation between activities and electronic structures was used to construct linear equation models. An equation model showing a good statistically criteria and a realibility of antimalarial activity was chosen to be used to design a compound with new activities against P. facilparum. Results show that 13 equation models were obtained, showing only three models with a good criteria. O2 and C4 atoms were observed for a key role of an improvement of the antimalarial activity.   Keywords: artemisinin, antimalaria, atomic charge, dipole moment, PM3

scholarly journals QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS OF CURCUMIN AND ITS DERIVATIVES AS GST INHIBITORS BASED ON COMPUTATIONAL CHEMISTRY CALCULATION

2010 ◽  
Vol 3 (3) ◽  
pp. 179-186 ◽  
Author(s):  
Enade Perdana Istyastono ◽  
Sudibyo Martono ◽  
Harno Dwi Pranowo ◽  
Iqmal Tahir
Keyword(s):  
Computational Chemistry ◽  
Linear Regression Analysis ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Equation Model ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Glutathione S Transferase ◽  
Relationship Analysis ◽  
Structure Activity

The Quantitative Structure-Activity Relationship (QSAR) study was established on curcumin and its derivatives as glutathione S-transferase(s) (GSTs) inhibitors using atomic net charges as the descriptors. The charges were resulted by semiempirical AM1 and PM3 quantum-chemical calculations using computational chemistry approach. The inhibition activity was expressed as the concentration that gave 50% inhibition of GSTs activity (IC50). The selection of the best QSAR equation models was determined by multiple linear regression analysis. This research was related to the nature of GSTs as multifunctional enzymes, which play an important role in the detoxification of electrophilic compounds, the process of inflammation and the effectivity of anticancer compounds. The result showed that AM1 semiempirical method gave better descriptor for the construction of QSAR equation model than PM3 did. The best QSAR equation model was described by : log 1/IC50 = -2,238 - 17,326 qC2' + 1,876 qC4' + 9,200 qC6' The equation was significant at 95% level with statistical parameters : n = 10, m = 3, r­ = 0,839, SE = 0,254, F = 4,764, F/Ftable = 1,001.   Keywords: QSAR analysis, curcumin, glutathione S-transferase(s) (GSTs), atomic net charge

scholarly journals Quantitative structure-activity relationship for antimalarial activity of artemisinin

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Rosmahaida Jamaludin ◽  
Mohamed Noor Hasan
Keyword(s):  
Regression Coefficient ◽  
Antimalarial Activity ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Data Set ◽  
Test Set ◽  
Structure Activity ◽  
New Compounds

The increase in resistance to older drugs and the emergence of new types of infection have created an urgent need for discovery and development of new compounds with antimalarial activity. Quantitative-Structure Activity Relationship (QSAR) methodology has been performed to develop models that correlate antimalarial activity of artemisinin analogs and their molecular structures. In this study, the data set consisted of 197 compounds with their activities expressed as log RA (relative activity). These compounds were randomly divided into training set (n=157) and test set (n=40). The initial stage of the study was the generation of a series of descriptors from three-dimensional representations of the compounds in the data set. Several types of descriptors which include topological, connectivity indices, geometrical, physical properties and charge descriptors have been generated. The number of descriptors was then reduced to a set of relevant descriptors by performing a systematic variable selection procedure which includes zero test, pairwisecorrelation analysis and genetic algorithm (GA). Several models were developed using different combinations of modelling techniques such as multiple linear regression (MLR) and partial least square (PLS) regression. Statistical significance of the final model was characterized by correlation coefficient, r2 and root-mean-square error calibration, RMSEC. The results obtained were comparable to those from previous study on the same data set with r2 values greater than 0.8. Both internal and external validations were carried out to verify that the models have good stability, robustness and predictive ability. The cross-validated regression coefficient (r2cv) and prediction regression coefficient (r2 test) for the external test set were consistently greater than 0.7. The QSAR models developed in this study should facilitate the search for new compounds with antimalarial activity.

scholarly journals Design of Trolox Compounds as Antioxidant and Their Analysis Using Quantitative Structure Activity Relationship

2020 ◽  
Vol 3 (2) ◽  
pp. 181
Author(s):  
Hasmalina Nasution ◽  
Nur Enizan ◽  
Nurlaili Nurlaili ◽  
Jufrizal Syahri
Keyword(s):  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Equation Model ◽  
The Body ◽  
Regression Methods ◽  
Structure Activity ◽  
Ic50 Value ◽  
Semi Empirical ◽  
Keywords Antioxidants

Antioxidant compound can inhibit the oxidation of lipids and other biomolecules. The role of antioxidants is very important in neutralizing and destroying free radicals that can cause the damage to cells in the body. This research was carried out to design trolox derivate compounds as antioxidants using the QSAR method. The semi empirical AM1(Austin Model 1)method was used to generate the QSAR model. The statistical analysis result using multiple linier regression methods revealed thet antioxidant activity was influenced by the descriptors of qC1, qC4, qO7, qC13 and qO18. The QSAR equation model obtained was log IC50 = 0.821 + 7.067 (qC1) + 2.585 (qC4) + 4.812 (qO7) – 5.363 (qC13) – 0.887 (qO18) with the best predicted IC50 value was 4.699 µM.   Keywords: Antioxidants, QSAR, semi empirical AM1, trolox

Sign in / Sign up

Export Citation Format

Share Document