Saíz-Urra Liane, Teijeira Marta, Rivero-Buceta Virginia, Helguera Aliuska Morales, Celeiro Maria, Terán Ma Carmen, Besada Pedro, Borges Fernanda
Laboratory for Medicinal Chemistry, Rega Institute for Medical Research, Katholieke Universiteit Leuven, Minderbroedersstraat 10, 3000, Leuven, Belgium.
Departamento de Química Orgánica. Facultade de Química, Universidade de Vigo, 36310, Vigo, Spain.
Mol Divers. 2016 Feb;20(1):55-76. doi: 10.1007/s11030-015-9617-z. Epub 2015 Jul 24.
Adenosine regulates tissue function by activating four G-protein-coupled adenosine receptors (ARs). Selective agonists and antagonists for A3 ARs have been investigated for the treatment of a variety of immune disorders, cancer, brain, and heart ischemic conditions. We herein present a QSAR study based on a Topological sub-structural molecular design (TOPS-MODE) approach, intended to predict the A3 ARs of a diverse dataset of 124 (94 training set/ 30 prediction set) adenosine derivatives. The final model showed good fit and predictive capability, displaying 85.1 % of the experimental variance. The TOPS-MODE approach afforded a better understanding and interpretation of the developed model based on the useful information extracted from the analysis of the contribution of different molecular fragments to the affinity.
腺苷通过激活四种G蛋白偶联腺苷受体(ARs)来调节组织功能。已经对A3 ARs的选择性激动剂和拮抗剂进行了研究,用于治疗各种免疫疾病、癌症、脑部和心脏缺血性疾病。我们在此提出一项基于拓扑亚结构分子设计(TOPS-MODE)方法的定量构效关系(QSAR)研究,旨在预测124种(94个训练集/30个预测集)腺苷衍生物的不同数据集中的A3 ARs。最终模型显示出良好的拟合度和预测能力,展示了85.1%的实验方差。基于从分析不同分子片段对亲和力的贡献中提取的有用信息,TOPS-MODE方法能够更好地理解和解释所开发的模型。