scholarly journals QSAR Modeling of Bisbenzofuran Compounds using 2D-Descriptors as Antimalarial Agents

2018 ◽  
Vol Volume-2 (Issue-2) ◽  
pp. 769-783
Author(s):  
Tripti Kaushal ◽  
Anita K ◽  
Bashirulla Shaik | Vijay K. Agrawal ◽  
2020 ◽  
Vol 32 (11) ◽  
pp. 2839-2845
Author(s):  
R. Hadanau

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).


2013 ◽  
Vol 9 (1) ◽  
pp. 95-107 ◽  
Author(s):  
Feng Luan ◽  
Xuan Xu ◽  
Maria Natalia Dias Soeiro Cordeiro ◽  
Huitao Liu ◽  
Xiaoyun Zhang

2013 ◽  
Vol 9 (1) ◽  
pp. 95-107
Author(s):  
Feng Luan ◽  
Xuan Xu ◽  
Maria Natalia Dias Soeiro Cordeiro ◽  
Huitao Liu ◽  
Xiaoyun Zhang

2007 ◽  
Author(s):  
Maykel González ◽  
Aliuska Helguera ◽  
M. Natália Cordeiro ◽  
Miguel Cabrera Pérez ◽  
Reinaldo Ruiz ◽  
...  

2020 ◽  
Vol 20 (14) ◽  
pp. 1389-1402 ◽  
Author(s):  
Maja Zivkovic ◽  
Marko Zlatanovic ◽  
Nevena Zlatanovic ◽  
Mladjan Golubović ◽  
Aleksandar M. Veselinović

In recent years, one of the promising approaches in the QSAR modeling Monte Carlo optimization approach as conformation independent method, has emerged. Monte Carlo optimization has proven to be a valuable tool in chemoinformatics, and this review presents its application in drug discovery and design. In this review, the basic principles and important features of these methods are discussed as well as the advantages of conformation independent optimal descriptors developed from the molecular graph and the Simplified Molecular Input Line Entry System (SMILES) notation compared to commonly used descriptors in QSAR modeling. This review presents the summary of obtained results from Monte Carlo optimization-based QSAR modeling with the further addition of molecular docking studies applied for various pharmacologically important endpoints. SMILES notation based optimal descriptors, defined as molecular fragments, identified as main contributors to the increase/ decrease of biological activity, which are used further to design compounds with targeted activity based on computer calculation, are presented. In this mini-review, research papers in which molecular docking was applied as an additional method to design molecules to validate their activity further, are summarized. These papers present a very good correlation among results obtained from Monte Carlo optimization modeling and molecular docking studies.


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