Kumar Ashwani, Chauhan Shilpi
Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India.
Arch Pharm (Weinheim). 2017 Jan;350(1). doi: 10.1002/ardp.201600268. Epub 2016 Dec 26.
SIRT1 inhibitors offer therapeutic potential for the treatment of a number of diseases including cancer and human immunodeficiency virus infection. A diverse series of 45 compounds with reported SIRT1 inhibitory activity has been employed for the development of quantitative structure-activity relationship (QSAR) models using the Monte Carlo optimization method. This method makes use of simplified molecular input line entry system notation of the molecular structure. The QSAR models were built up according to OECD principles. Three subsets of three splits were examined and validated by respective external sets. All the three described models have good statistical quality. The best model has the following statistical characteristics: R = 0.8350, Q = 0.7491 for the test set and R = 0.9655, Q = 0.9261 for the validation set. In the mechanistic interpretation, structural attributes responsible for the endpoint increase and decrease are defined. Further, the design of some prospective SIRT1 inhibitors is also presented on the basis of these structural attributes.
SIRT1抑制剂为包括癌症和人类免疫缺陷病毒感染在内的多种疾病的治疗提供了潜在的治疗方法。已使用一系列45种具有报道的SIRT1抑制活性的化合物，采用蒙特卡罗优化方法开发定量构效关系（QSAR）模型。该方法利用分子结构的简化分子输入线性条目系统表示法。QSAR模型是根据经合组织原则建立的。对三个拆分的三个子集进行了检查，并通过各自的外部集进行了验证。所描述的所有三个模型都具有良好的统计质量。最佳模型具有以下统计特征：测试集的R = 0.8350，Q = 0.7491；验证集的R = 0.9655，Q = 0.9261。在机理解释中，定义了导致终点增加和减少的结构属性。此外，还基于这些结构属性提出了一些前瞻性SIRT1抑制剂的设计。
