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三环恶唑烷酮类抗菌剂的三维定量构效关系(3D-QSAR)研究

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic oxazolidinones as antibacterial agents.

作者信息

Gopalakrishnan Bulusu, Khandelwal Akash, Rajjak Shaikh Abdul, Selvakumar Natesan, Das Jagattaran, Trehan Sanjay, Iqbal Javed, Kumar Magadi Sitaram

机构信息

Department of Molecular Modeling and Drug Design, Dr. Reddy's Laboratories Ltd., Discovery Research, Bollaram Road, Miyapur, Hyderabad 500 050, India.

出版信息

Bioorg Med Chem. 2003 Jun 12;11(12):2569-74. doi: 10.1016/s0968-0896(03)00157-3.

Abstract

Oxazolidinones exemplified by eprezolid and linezolid are a new class of antibacterials that are active against Gram positive and anaerobic bacteria including methicillin-resistant Staphylococcus aureus (MRSA), methicillin-resistant Staphylococcus epidermidis (MRSE) and vancomycin resistant enterococci (VRE). In an effort to have a better antibacterial agent in the oxazolidinone class, we have performed three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for a series of tricyclic oxazolidinones. 3D-QSAR studies were performed using the Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) procedures. These studies were performed using 42 compounds; the QSAR model was developed using a training set of 33 compounds. The predictive ability of the QSAR model was assessed using a test set of 9 compounds. The predictive 3D-QSAR models have conventional r(2) values of 0.975 and 0.940 for CoMFA and CoMSIA respectively; similarly, cross-validated coefficient q(2) values of 0.523 and 0.557 for CoMFA and CoMSIA, respectively, were obtained. The CoMFA 3D-QSAR model performed better than the CoMSIA model.

摘要

以依哌唑胺和利奈唑胺为代表的恶唑烷酮类是一类新型抗菌药物,对革兰氏阳性菌和厌氧菌具有活性,包括耐甲氧西林金黄色葡萄球菌(MRSA)、耐甲氧西林表皮葡萄球菌(MRSE)和耐万古霉素肠球菌(VRE)。为了在恶唑烷酮类中获得更好的抗菌剂,我们对一系列三环恶唑烷酮进行了三维定量构效关系(3D-QSAR)研究。3D-QSAR研究采用比较分子场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)方法进行。这些研究使用了42种化合物;QSAR模型是使用33种化合物的训练集开发的。使用9种化合物的测试集评估QSAR模型的预测能力。预测性3D-QSAR模型的CoMFA和CoMSIA的传统r(2)值分别为0.975和0.940;类似地,CoMFA和CoMSIA的交叉验证系数q(2)值分别为0.523和0.557。CoMFA 3D-QSAR模型的表现优于CoMSIA模型。

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