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首页> 外文期刊>European Journal of Medicinal Chemistry: Chimie Therapeutique >Insights into the structural requirements of farnesyltransferase inhibitors as potential anti-tumor agents based on 3D-QSAR CoMFA and CoMSIA models.
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Insights into the structural requirements of farnesyltransferase inhibitors as potential anti-tumor agents based on 3D-QSAR CoMFA and CoMSIA models.

机译：基于3D-QSAR CoMFA和CoMSIA模型，深入研究了法呢基转移酶抑制剂作为潜在抗肿瘤药物的结构要求。

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A three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on three different chemical series reported as selective farnesyltransferase (FTase) inhibitors employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) techniques to investigate the structural requirements for substrates and derive a predictive model that may be used for the design of novel FTase inhibitors. Removal of outliers improved the predictive power of models developed for all three structurally diverse classes of compounds. 3D-QSAR models were derived for 3-aminopyrrolidinone derivatives (training set N=38, test set N=7), 2-amino-nicotinonitriles (training set N=46, test set N=13) and 1-aryl-1'-imidazolyl methyl ethers (training set N=35, test set N=5). The CoMFA models with steric and electrostatic fields exhibited r(2)(cv) 0.479-0.803, r(2)(ncv) 0.945-0.993, r(2)(pred) 0.686-0.811. The CoMSIA models displayed r(2)(cv) 0.411-0.814, r(2)(ncv) 0.923-0.984, r(2)(pred) 0.399-0.787. 3D contour maps generated from these models were analyzed individually, which provide the regions in space where interactive fields may influence the activity. The superimposition of contour maps on the active site of farnesyltransferase additionally helps in understanding the structural requirements of these inhibitors. 3D-QSAR models developed may guide our efforts in designing and predicting the FTase inhibitory activity of novel molecules.
机译：使用比较分子场分析（CoMFA）和比较分子相似性指数（CoMSIA）技术，对报道为三种选择性法呢基转移酶（FTase）抑制剂的三个不同化学系列进行了三维定量构效关系（3D-QSAR）研究。底物的结构要求，并推导出可用于设计新型FTase抑制剂的预测模型。消除异常值提高了针对所有三种结构上不同的化合物类别开发的模型的预测能力。推导了3-氨基吡咯烷酮衍生物（训练组N = 38，测试组N = 7），2-氨基烟腈（训练组N = 46，测试组N = 13）和1-芳基-1'的3D-QSAR模型。 -咪唑基甲基醚（训练组N ＝ 35，测试组N ＝ 5）。具有空间和静电场的CoMFA模型显示r（2）（cv）0.479-0.803，r（2）（ncv）0.945-0.993，r（2）（pred）0.686-0.811。 CoMSIA模型显示r（2）（cv）0.411-0.814，r（2）（ncv）0.923-0.984，r（2）（pred）0.399-0.787。分别分析了从这些模型生成的3D等高线图，这些图提供了交互作用场可能影响活动的空间区域。轮廓图在法呢基转移酶的活性位点上的叠加另外有助于理解这些抑制剂的结构要求。开发的3D-QSAR模型可以指导我们设计和预测新型分子的FTase抑制活性。

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