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3D QSAR pharmacophore modeling for c-Met kinase inhibitors

机译:c-Met激酶抑制剂的3D QSAR药效团建模

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摘要

The receptor tyrosine kinase c-Met has multiple roles during cancer development and is currently considered as a promising target for cancer therapies. Pharmacophore models of c-Met kinase inhibitors have been developed based on 22 diverse compounds by using HypoGen algorithm implemented in Discovery studio program package. The best quantitative pharmacophore model, Hypo 1, which had the highest correlation coefficient (0.9623), consists of two hydrogen bond acceptors, one hydrophobic feature and two excluded volumes. Then best model was validated by test set prediction, Fischer randomization and decoy set. Besides, the features of Hypo1 were verified to correctly reflect the interactions between kinase active site and its ligands by comparison and superimposition of Hypo 1 in active site of c-Met kinase. The results shows that Hypo 1 has strong capability to identify c-Met kinase inhibitors and to predict the activities of structurally diverse molecules. Therefore, our pharmacophore models were considered as valuable tools for the discovery and development of specific c-Met kinase inhibitors.
机译:受体酪氨酸激酶c-Met在癌症发展过程中具有多种作用,目前被认为是癌症治疗的有希望的靶标。通过使用在Discovery Studio程序包中实现的HypoGen算法,基于22种化合物开发了c-Met激酶抑制剂的药理模型。最佳的定量药效团模型Hypo 1具有最高的相关系数(0.9623),它由两个氢键受体,一个疏水特征和两个排除体积组成。然后通过测试集预测,Fischer随机化和诱饵集验证最佳模型。此外,通过比较和叠加Hypo 1在c-Met激酶活性位点上,验证了Hypo1的特征正确反映了激酶活性位点与其配体之间的相互作用。结果表明,Hypo 1具有强大的能力来识别c-Met激酶抑制剂并预测结构多样的分子的活性。因此,我们的药效团模型被认为是发现和开发特定c-Met激酶抑制剂的有价值的工具。

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