Coi Alessio, Fiamingo Francesca Lidia, Livi Oreste, Calderone Vincenzo, Martelli Alma, Massarelli Ilaria, Bianucci Anna Maria
Dipartimento di Scienze Farmaceutiche, Università di Pisa, Via Bonanno 6, 56126 Pisa, Italy.
Bioorg Med Chem. 2009 Jan 1;17(1):319-25. doi: 10.1016/j.bmc.2008.10.068. Epub 2008 Nov 5.
QSAR studies were developed on the basis of a dataset comprising BK channel activators previously synthesized and biologically assayed in our laboratory, in order to obtain highly accurate models enabling prediction of affinity toward the channel for New Chemical Entities (NCEs). Many molecular descriptors were computed by the CODESSA software. They were initially exploited in order to rationally split the available dataset into training and test set pairs, which supplied the basis for the development of QSAR models. Models were subjected to rigorous validation analysis based on the estimate of several statistical parameters, for the seek of the most accurate and simplest model enabling prediction of BK channel affinity.
定量构效关系（QSAR）研究是基于一个数据集开展的，该数据集包含我们实验室之前合成并进行过生物学检测的BK通道激活剂，目的是获得高度准确的模型，以便预测新化学实体（NCEs）对该通道的亲和力。许多分子描述符由CODESSA软件计算得出。最初利用这些描述符是为了合理地将可用数据集划分为训练集和测试集对，这为QSAR模型的开发提供了基础。基于多个统计参数的估计对模型进行了严格的验证分析，以寻找能够预测BK通道亲和力的最准确、最简单的模型。
