Du Lü-Pei, Tsai Keng-Chang, Li Min-Yong, You Qi-Dong, Xia Lin
Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, China.
Bioorg Med Chem Lett. 2004 Sep 20;14(18):4771-7. doi: 10.1016/j.bmcl.2004.06.070.
Predictive pharmacophore models were developed for a large series of I(Kr) potassium channel blockers as class III antiarrhythmic agents using HypoGen in Catalyst software. The pharmacophore hypotheses were generated using a training set consisting of 34 compounds carefully selected from documents. Their biological data, expressed as IC(50), spanned from 1.5 nM to 2.8 mM with 7 orders difference. The most predictive hypothesis (Hypo1), consisting of four features (one positive ionizable feature, two aromatic rings and one hydrophobic group), had a best correlation coefficient of 0.825, a lowest rms deviation of 1.612, and a highest cost difference (null cost-total cost) of 77.552, which represents a true correlation and a good predictivity. The hypothesis Hypo1 was then validated by a test set consisting of 21 compounds and by a cross-validation of 95% confidence level with randomizing the data using CatScramble program. Accordingly, our model has strong predictivity to identify structural diverse I(Kr) potassium channel blockers with desired biological activity by virtual screening
使用Catalyst软件中的HypoGen,为作为III类抗心律失常药物的一大系列I(Kr)钾通道阻滞剂开发了预测性药效团模型。药效团假设是使用一个训练集生成的,该训练集由从文献中精心挑选的34种化合物组成。它们的生物学数据以IC(50)表示,范围从1.5 nM到2.8 mM,相差7个数量级。最具预测性的假设(Hypo1)由四个特征组成(一个正可电离特征、两个芳香环和一个疏水基团),最佳相关系数为0.825,最低均方根偏差为1.612,最高成本差异(零成本 - 总成本)为77.552,这代表了真实的相关性和良好的预测性。然后通过由21种化合物组成的测试集以及使用CatScramble程序对数据进行随机化的95%置信水平的交叉验证来验证假设Hypo1。因此,我们的模型具有很强的预测性,能够通过虚拟筛选识别具有所需生物活性的结构多样的I(Kr)钾通道阻滞剂。