Drug Theoretics and Cheminformatics Lab, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.
J Mol Model. 2010 Oct;16(10):1585-96. doi: 10.1007/s00894-010-0661-4. Epub 2010 Mar 1.
Predictive pharmacophore models have been developed for a series of arylamino-substituted benzo[b]thiophenes exhibiting free radical scavenging activity. 3D pharmacophore models were generated using a set of 20 training set compounds and subsequently validated by mapping 6 test set compounds using Discovery Studio 2.1 software. Further model validation was performed by randomizing the data using Fischer's validation technique at the 95% confidence level. The most predictive pharmacophore model developed using the conformers obtained from the BEST method showed a correlation coefficient (r) of 0.942 and consisted of three features: hydrogen bond donor, hydrogen bond acceptor and aromatic ring. Acceptable values of external validation parameters, like R2pred (0.853) and r2m(test) (0.844), also implied that the external predictivity of the model was significant. The development of further pharmacophore models using conformers obtained from the FAST method yielded a few models with good predictivity, with the best one (r=0.904) consisting of two features: hydrogen bond donor and hydrogen bond acceptor. Significant values of external validation parameters, R2pred (0.913) and r2m(test) (0.821), also reflect the high predictive ability of the model. Again, Fischer validation results implied that the models developed were robust enough and their good results were not based on mere chance. These validation approaches indicate the reliability of the predictive abilities of the 3D pharmacophore models developed here, which may thus be further utilized as a 3D query tool in the virtual screening of new chemical entities with potent antioxidant activities.
已经为具有自由基清除活性的一系列芳基氨基取代的苯并[b]噻吩类化合物开发了预测药效基团模型。使用一组 20 个训练集化合物生成了 3D 药效基团模型,然后使用 Discovery Studio 2.1 软件通过映射 6 个测试集化合物来验证该模型。进一步通过在 95%置信水平下使用 Fischer 验证技术随机化数据来验证模型。使用 BEST 方法获得的构象体开发的最具预测性的药效基团模型显示出 0.942 的相关系数(r),并包含三个特征:氢键供体、氢键受体和芳环。可接受的外部验证参数值,如 R2pred(0.853)和 r2m(test)(0.844),也表明模型的外部预测能力是显著的。使用 FAST 方法获得的构象体开发进一步的药效基团模型得到了一些具有良好预测能力的模型,其中最好的一个模型(r=0.904)由两个特征组成:氢键供体和氢键受体。外部验证参数 R2pred(0.913)和 r2m(test)(0.821)的显著值也反映了模型的高预测能力。再次,Fischer 验证结果表明,所开发的模型足够稳健,其良好的结果并非仅仅基于偶然。这些验证方法表明,这里开发的 3D 药效基团模型的预测能力是可靠的,因此可以进一步用作具有潜在抗氧化活性的新化学实体虚拟筛选的 3D 查询工具。