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针对恶性疟原虫抗疟效力的多种喹诺酮化合物的分类SAR建模

Classification SAR modeling of diverse quinolone compounds for antimalarial potency against Plasmodium falciparum.

作者信息

Aher Rahul Balasaheb, Roy Kunal

机构信息

Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.

出版信息

Comb Chem High Throughput Screen. 2014;17(5):396-406. doi: 10.2174/1386207316666131230093802.

Abstract

Both a development of resistance to artemisinin monotherapy and lack of effective vaccine against malaria have created the urgent need for the development of new and efficient antimalarial agents. In this background, we have developed here a linear discriminant analysis (LDA) model and a few 3D-pharmacophore models for the classification of diverse quinolone compounds based on their antimalarial potency against Plasmodium falciparum. The discriminant model shows 70% correct classification for the test set compounds into higher active and lower active analogues. The best pharmacophore model (Hypo-1) with a correlation coefficient of 0.83 shows one hydrogen bond acceptor (HBA) and two ring aromatic (RA) features as the essential structural requirements for antimalarial activity against P falciparum. Both the models may act as in silico filters for a virtual screening and could be utilized for the selection of higher active molecules falling within the applicability of the models.

摘要

对青蒿素单一疗法产生耐药性以及缺乏有效的疟疾疫苗,都迫切需要开发新的高效抗疟药物。在此背景下,我们基于喹诺酮类化合物对恶性疟原虫的抗疟效力,开发了一种线性判别分析(LDA)模型和一些三维药效团模型,用于对多种喹诺酮化合物进行分类。判别模型对测试集化合物的正确分类率为70%,可将其分为高活性和低活性类似物。最佳药效团模型(Hypo-1)的相关系数为0.83,显示出一个氢键受体(HBA)和两个环芳香(RA)特征是对恶性疟原虫具有抗疟活性的基本结构要求。这两种模型都可作为虚拟筛选的计算机筛选过滤器,并可用于选择符合模型适用范围的高活性分子。

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