Debeljak Zeljko, Skrbo Armin, Jasprica Ivona, Mornar Ana, Plecko Vanda, Banjanac Mihajlo, Medić-Sarić Marica
Department of Medicinal Biochemistry, Clinical Hospital Osijek, J. Huttlera 4, 31000 Osijek, Croatia.
J Chem Inf Model. 2007 May-Jun;47(3):918-26. doi: 10.1021/ci600473z. Epub 2007 May 10.
A new class of antimicrobial agents, 3-nitrocoumarins and related compounds, has been chosen as a subject of the present study. In order to explore their activity and molecular properties that determine their antimicrobial effects, QSAR models have been proposed. Most of the 64 descriptors used for the development were extracted from semiempirical and density functional theory (DFT) founded calculations. For this study literature data containing results of microbiological activity screening of 33 coumarin derivatives against selected clinical isolates of C. albicans (CA) and S. aureus (SA) have been selected. Multivariate predictive models based on random forests (RF) and two hybrid classification approaches, genetic algorithms (GA) associated with either support vector machines (SVM) or k nearest neighbor (kNN), have been used for establishment of QSARs. An applied feature selection approach enabled two-dimensional linear separation of active and inactive compounds, which was a necessary tool for rational candidate design and descriptor relevance interpretation. Candidate molecules were checked by cross-validated models, and selected derivatives have been synthesized. Their antimicrobial activities were compared to antimicrobial activities of the representative derivatives from the original set in terms of minimal inhibitory concentration (MIC) against chosen SA and CA ATCC strains. High ranking of descriptors consistent with the degree of hydrolytic instability of selected compounds is common to models of antimicrobial activity against both microorganisms. However, descriptor ranking indicates different antimicrobial mechanisms of action of chosen coumarin derivatives against selected microbial species.
一类新型抗菌剂，即3-硝基香豆素及其相关化合物，已被选为当前研究的主题。为了探究它们的活性以及决定其抗菌效果的分子特性，已提出了定量构效关系（QSAR）模型。用于模型构建的64个描述符大多是从基于半经验和密度泛函理论（DFT）的计算中提取的。对于本研究，已选取了包含33种香豆素衍生物对白色念珠菌（CA）和金黄色葡萄球菌（SA）的选定临床分离株进行微生物活性筛选结果的文献数据。基于随机森林（RF）以及两种混合分类方法，即与支持向量机（SVM）或k近邻（kNN）相关联的遗传算法（GA），构建了多变量预测模型以建立QSARs。所应用的特征选择方法实现了活性和非活性化合物的二维线性分离，这是合理候选设计和描述符相关性解释的必要工具。通过交叉验证模型对候选分子进行检验，并合成了选定的衍生物。根据对选定的SA和CA ATCC菌株的最低抑菌浓度（MIC），将它们的抗菌活性与原始组中代表性衍生物的抗菌活性进行了比较。与选定化合物的水解不稳定性程度一致的描述符的高排名在针对这两种微生物的抗菌活性模型中是常见的。然而，描述符排名表明选定的香豆素衍生物对选定微生物物种具有不同的抗菌作用机制。
