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3D QSAR studies of dioxins and dioxin-like compounds using CoMFA and CoMSIA

机译:使用CoMFA和CoMSIA对二恶英和二恶英类化合物进行3D QSAR研究

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In the present study we have performed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) on structurally diverse ligands of Ah (dioxin) receptor to explore the physico-chemical requirements for binding. All CoMFA and CoMSIA models have given q~2 value of more than 0.5 and r~2 value of more than 0.84. The predictive ability of the models was validated by an external test set, which gave satisfactory predictive r~2 values. Best predictions were obtained with CoMFA model of combined modified training set (q~2 = 0.631,r~2 = 0.900), giving predictive residual value = 0.02 log unit for the test compound. Addition of CoMSIA study has elucidated the role of hydrophobicity and hydrogen bonding along with the effect of steric and electrostatic properties revealed by CoMFA. We have suggested a model comprises of four structurally different compounds, which offers a good predictability for various ligands. Our QSAR model is consistent with all previously established QSAR models with less structurally diverse ligands.
机译:在本研究中,我们对Ah(二恶英)受体的结构多样的配体进行了比较分子场分析(CoMFA)和比较分子相似性指数分析(CoMSIA),以探索结合的物理化学要求。所有CoMFA和CoMSIA模型的q〜2值均大于0.5,r〜2值均大于0.84。模型的预测能力通过外部测试集验证,该模型给出了令人满意的r〜2预测值。使用组合修改的训练集的CoMFA模型可获得最佳预测(q〜2 = 0.631,r〜2 = 0.900),得出测试化合物的预测残差= 0.02 log单位。 CoMSIA研究的更多内容阐明了疏水性和氢键的作用以及CoMFA揭示的空间和静电性质的影响。我们已经提出了一种模型,该模型包含四种结构不同的化合物,这些化合物为各种配体提供了良好的可预测性。我们的QSAR模型与所有先前建立的QSAR模型都具有相同的结构,这些QSAR模型具有较少的结构多样性的配体。

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