Ojha Probir Kumar, Roy Kunal
Drug Theoretics and Cheminformatics Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India.
Curr Comput Aided Drug Des. 2013 Sep;9(3):336-49. doi: 10.2174/15734099113099990019.
The emergence of multidrug resistance of the currently available antimalarial drugs has led to the need of the discovery and development of new antimalarial compounds. In the present study, we have used a novel group based quantitative structure-activity relationship (G-QSAR) approach, which allows to establish a correlation of chemical group variation at different molecular sites of interest with the biological activity, using a series of 53 antimalarial endochin analogs. In our previous work, we developed QSAR models for this data set using different chemometric tools and tried to emphasize on importance of descriptor thinning and noise reduction prior to feature selection step. In the present paper, we have tried to select optimal subset of variables using a new strategy for the development of robust G-QSAR models. Starting with an initial pool of 6395 descriptors, we have finally used 51 descriptors for model development using genetic methods. The best model showed encouraging values for internal.....
目前可用抗疟药物多重耐药性的出现,导致了发现和开发新型抗疟化合物的需求。在本研究中,我们使用了一种基于基团的新型定量构效关系(G-QSAR)方法,该方法利用一系列53种抗疟氯胍类似物,能够建立感兴趣的不同分子位点上化学基团变化与生物活性之间的相关性。在我们之前的工作中,我们使用不同的化学计量工具为该数据集开发了QSAR模型,并试图强调在特征选择步骤之前描述符精简和降噪的重要性。在本文中,我们尝试使用一种新策略来选择变量的最优子集,以开发稳健的G-QSAR模型。从6395个描述符的初始库开始,我们最终使用遗传方法选择了51个描述符用于模型开发。最佳模型显示出令人鼓舞的内部......值。