Pharmacophore Screening on Piperidinecarboxamides Derivatives Based on GALAHAD and CoMFA Models

In order to discover the novel anticonvulsant drugs, pharmacophore screening of the anticonvulsant inhibitors was enforced. Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets （GALAHAD） and Comparative Molecular Field Analysis （CoMFA） studies were combined to implement our research. Firstly, multiple models were generated using GALAHAG based on high active molecules. Secondly, several of them were validated using the CoMFA study. Finally, a good values of q2 from training set and promising predictive power from test set were obtained based on one model simutaneously. One model had been selected as the most reasonable pharmacophore model. The results of the CoMFA study based on the model 1 suggested that both steric and electrostatic interactions played important roles.

抗阿尔茨海默病活性GSK-3β抑制剂药效团筛选模型构建及验证

目的构建抗阿尔茨海默病活性GSK-3β抑制剂药效团筛选模型。方法利用GALAHAD药效团模块构建GSK-3β抑制剂药效团模型。通过药效团模型筛选方法,验证药效团模型的准确性。结果共获得20个GSK-3β抑制剂药效团模型,其中药效团模型18的富集率为111.76%,是所有药效团模型中最高。药效团模型18与10个训练集分子均能很好地拟合。结论药效团模型18可以作为GSK-3β抑制剂药效团筛选模型。