Human health has been severely affected by infections resulting from multidrug-resistant (MDR) gram-negative bacteria (GNB). Monobactam antibiotics are known to be effective against such infections. This study aimed to construct a predictive two-dimensional quantitative structure-activity relationship (2D-QSAR) model for the rational design of new monobactams based on the 65 known monobactams against Escherichia coli (Eco) and Klebsiella pneumonia (Kpn) strains using the kernel partial least squares regression (KPLS) algorithm. The total performance of Eco and Kpn KPLS modes was shown as RMSE: 0.681/0.596, R²: 0.946/0.882, Q²: 0.922/0.877, and RMSU: 0.625/0.593. Thirty-four monobactams reported in our lab were chosen as external data to predict their activities against Eco and Kpn using the newly established models, by which the R² between the experimental and predicted values was 0.878 and 0.871, respectively. The models developed and verified in this study provide a powerful design strategy for novel monobactams that are effective against MDR gram-negative bacterial infections.

中文翻译：

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单克隆细菌类似物对革兰氏阴性细菌的全面二维定量构效关系研究。

多重耐药（MDR）革兰氏阴性菌（GNB）引起的感染已严重影响人类健康。已知单bactam抗生素可有效抵抗此类感染。本研究旨在基于内核已知的65种已知的针对大肠杆菌（Eco）和肺炎克雷伯氏菌（Klebsiella pneumonia）（Kpn）菌株的单杆菌素，为新的单杆菌素的合理设计构建预测性二维定量构效关系（2D-QSAR）模型。偏最小二乘回归（KPLS）算法。的总性能生态和KPN KPLS模式被示出为RMSE：0.681 / 0.596，- [R ²：0.946 / 0.882，Q²：0.922 / 0.877，RMSU：0.625 / 0.593。在我们的实验室报告三十四个单环内酰胺被选为外部数据来预测其对活动生态和KPN使用新建立的模型，通过该ř实验值和预测值之间²分别为0.878和0.871。在这项研究中开发和验证的模型为有效抵抗MDR革兰氏阴性细菌感染的新型单bactams提供了强大的设计策略。