Bucinski Adam, Markuszewski Michal Jan, Wiktorowicz Wlodzimierz, Krysinski Jerzy, Kaliszan Roman
Division of Food Science, Institute of Animal Reproduction & Food Research, Polish Acaademy of Sciences, Olsztyn.
Comb Chem High Throughput Screen. 2004 Jun;7(4):327-36. doi: 10.2174/1386207043328652.
Artificial neural networks (ANNs) have been applied for the quantitative structure-activity relationships (QSAR) studies of antibacterial activity against Escherichia coli, Serratia marcescens, Proteus vulgaris, Klebsiella pneumoniae and Pseudomonas aeruginosa of a large series of new imidazole derivatives. Antibacterial activity against individual bacteria, expressed as logarithm of reciprocal of the minimal inhibitory concentrations, log 1/MIC, has been related to a number of physicochemical and structural parameters of the imidazole derivatives investigated. Molecular descriptors of agents were obtained by quantum-chemical calculations combined with molecular modelling and from respective structure fragment reference data (e.g., log P). A high correlation resulted between the predicted from ANN model antibacterial activity, log 1/MIC(ANN), and that from biological experiments, log 1/MIC(exp), both for the data used in learning and in the testing sets of imidazoles. Correlation coefficient, R, depending on the type of bacteria and structural subset of analysed imidazole compounds, varies from 0.875 to 0.969. The applicability of ANNs has been demonstrated for the prediction of pharmacological potency of new imidazole derivatives based on their structural descriptors generated exclusively by calculation chemistry.
人工神经网络（ANNs）已被应用于一系列新型咪唑衍生物对大肠杆菌、粘质沙雷氏菌、普通变形杆菌、肺炎克雷伯菌和铜绿假单胞菌抗菌活性的定量构效关系（QSAR）研究。针对单个细菌的抗菌活性，以最低抑菌浓度的倒数的对数表示，即log 1/MIC，已与所研究的咪唑衍生物的一些物理化学和结构参数相关。通过量子化学计算结合分子建模以及从各自的结构片段参考数据（例如log P）中获得药剂的分子描述符。对于咪唑学习集和测试集中的数据，ANN模型预测的抗菌活性log 1/MIC(ANN)与生物学实验得到的抗菌活性log 1/MIC(exp)之间产生了高度相关性。取决于细菌类型和所分析的咪唑化合物的结构子集，相关系数R在0.875至0.969之间变化。基于仅由计算化学生成的结构描述符，ANNs在预测新型咪唑衍生物的药理活性方面的适用性已得到证明。
