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QSAR and Pharmacophore Modeling of Nitrogen Heterocycles as Potent Human N-Myristoyltransferase (Hs-NMT) Inhibitors

机译:氮杂杂环的QSAR和药效线建模作为有效的人N-Myristoyltransferase(HS-NMT)抑制剂

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摘要

N-myristoyltransferase (NMT) is an important eukaryotic monomeric enzyme which has emerged as an attractive target for developing a drug for cancer, leishmaniasis, ischemia-reperfusion injury, malaria, inflammation, etc. In the present work, statistically robust machine leaning models (QSAR (Quantitative Structure–Activity Relationship) approach) for Human NMT (Hs-NMT) inhibitory has been performed for a dataset of 309 Nitrogen heterocycles screened for NMT inhibitory activity. Hundreds of QSAR models were derived. Of these, the model 1 and 2 were chosen as they not only fulfil the recommended values for a good number of validation parameters (e.g., R2 = 0.77–0.79, Q2LMO = 0.75–0.76, CCCex = 0.86–0.87, Q2-F3 = 0.74–0.76, etc.) but also provide useful insights into the structural features that sway the Hs-NMT inhibitory activity of Nitrogen heterocycles. That is, they have an acceptable equipoise of descriptive and predictive qualities as per Organisation for Economic Co-operation and Development (OECD) guidelines. The developed QSAR models identified a good number of molecular descriptors like solvent accessible surface area of all atoms having specific partial charge, absolute surface area of Carbon atoms, etc. as important features to be considered in future optimizations. In addition, pharmacophore modeling has been performed to get additional insight into the pharmacophoric features, which provided additional results.
机译:n-myristoyltransferase(nmt)是一种重要的真核单体酶,其作为发育癌症,Leishmaniaisis,缺血再灌注损伤,疟疾,炎症等药物的有吸引力的靶标。在目前的工作中,统计上强大的机器倾斜模型(对人NMT(HS-NMT)抑制的QSAR(定量结构 - 活性关系)方法已经对309个氮杂杂环的数据集进行了筛选的NMT抑制活性。派生了数百个QSAR模型。其中,选择了模型1和2,因为它们不仅满足了良好数量的验证参数(例如,R2 = 0.77-0.79,Q2LMO = 0.75-0.76,CCCEX = 0.86-0.87,Q2-F3 =的推荐值0.74-0.76等),但还提供了对摇摆HS-NMT抑制活性的结构特征的有用见解。也就是说,根据经济合作和发展(经合组织)指南,他们具有可接受的具有描述性和预测品质的装备品质。开发的QSAR模型鉴定了许多分子描述符,如具有特定部分电荷,碳原子的绝对表面积等的所有原子的溶剂可接近表面区域等。作为未来优化所考虑的重要特征。此外,已经进行了药物光线建模,以获得额外的洞察药物功能,提供额外的结果。

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