Sakhteman Amirhossein, Edraki Najmeh, Hemmateenejad Bahram, Miri Ramin, Foroumadi Alireza, Shafiee Abbas, Khoshneviszadeh Mehdi
Department of Medicinal Chemistry, Faculty of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran.
Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Iran J Pharm Res. 2017 Spring;16(2):513-524.
The IL-1β plays a major role in inflammatory disorders and IL-1β production inhibitors can be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 46 pyridazine derivatives (inhibitors of IL-1β production) and their activities were investigated by Multiple Linear Regression (MLR) technique Stepwise Regression Method (ES-SWR). The genetic algorithm (GA) has been proposed for improvement of the performance of the MLR modeling by choosing the most relevant descriptors. The results show that eight descriptors are able to describe about 83.70% of the variance in the experimental activity of the molecules in the training set. The physical meaning of the selected descriptors is discussed in detail. Power predictions of the QSAR models developed were evaluated using cross-validation, and validation through an external prediction set. The results showed satisfactory goodness-of-fit, robustness and perfect external predictive performance. The applicability domain was used to define the area of reliable predictions. Furthermore, the screening technique was applied in order to predict the structure and potency of new compounds of this type using the proposed QSAR model.
白细胞介素-1β在炎症性疾病中起主要作用，白细胞介素-1β产生抑制剂可用于治疗炎症及相关疾病。在本研究中，采用逐步回归法（ES-SWR）的多元线性回归（MLR）技术研究了46种哒嗪衍生物（白细胞介素-1β产生抑制剂）的结构与其活性之间的定量关系。已提出遗传算法（GA），通过选择最相关的描述符来提高MLR建模的性能。结果表明，八个描述符能够描述训练集中分子实验活性中约83.70%的方差。详细讨论了所选描述符的物理意义。使用交叉验证对所开发的QSAR模型进行幂预测，并通过外部预测集进行验证。结果显示出令人满意的拟合优度、稳健性和完美的外部预测性能。使用适用域来定义可靠预测的区域。此外，应用筛选技术以使用所提出的QSAR模型预测此类新化合物的结构和效力。
