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Quantitative structure-activity relationship (QSAR) and design of novel ligands that demonstrate high potency and target selectivity as protein tyrosine phosphatase 1B (PTP 1B) inhibitors as an effective strategy used to model anti-diabetic agents
Journal of Receptors and Signal Transduction ( IF 2.8 ) Pub Date : 2020-05-12 , DOI: 10.1080/10799893.2020.1759092
David Ebuka Arthur 1, 2 , Stephen Ejeh 3 , Adamu Uzairu 3
Affiliation  

Abstract Diabetes and obesity have increased dramatically in recent decades worldwide. Diabetes mainly emerged as a major health care burden disease in both the US and other industrialized countries, among which type II diabetes is the most common. Discovering new and effective treatments for diabetes is currently a high international health priority. In the present study a computational technique was used to model 97 compounds with PTP-1B inhibitory activity, in order to demonstrate the Quantitative structure-activity relationship (QSAR) of these compounds a genetic function approximation (GFA) algorithm was applied to pick the best descriptors and multiple linear regression (MLR) was used to establish a relationship between the PTP-1B inhibitory activity of these compounds and the best molecular descriptors. This QSAR study allowed investigating the influence of very simple and easy-to-compute descriptors in determining biological activities, which shed light on the key factors that aid in the design of novel potent molecules using computer-aided drug design tools.

中文翻译:

定量构效关系 (QSAR) 和新型配体的设计,这些配体显示出作为蛋白质酪氨酸磷酸酶 1B (PTP 1B) 抑制剂的高效力和靶向选择性,是一种用于模拟抗糖尿病药物的有效策略

摘要 近几十年来,糖尿病和肥胖症在全球范围内急剧增加。在美国和其他工业化国家,糖尿病主要成为主要的医疗保健负担疾病,其中 II 型糖尿病最为常见。发现新的有效的糖尿病治疗方法是目前国际上的一个高度优先的健康问题。在本研究中,使用计算技术对 97 种具有 PTP-1B 抑制活性的化合物进行建模,为了证明这些化合物的定量构效关系 (QSAR),应用遗传函数近似 (GFA) 算法来选择最佳化合物描述符和多元线性回归 (MLR) 用于建立这些化合物的 PTP-1B 抑制活性与最佳分子描述符之间的关系。
更新日期:2020-05-12
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