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Integrating docking scores, interaction profiles and molecular descriptors to improve the accuracy of molecular docking: Toward the discovery of novel Akt1 inhibitors

机译:整合对接分数,相互作用谱和分子描述符以提高分子对接的准确性:寻求新型Akt1抑制剂

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摘要

A set of forty-seven Akt1 inhibitors was used for the development of molecular docking based QSAR model by using nonlinear regression. The integration of docking scores, key interaction profiles and molecular descriptors remarkably improved the accuracy of the QSAR models, providing reasonable statistical parameters (Rtrain 2 = 0.948, R test 2 = 0.907 and Qcv 2 = 0.794). The established MD-SVR model based structural modification of new 4-amino-pyrimidine derivatives was further performed, and six compounds 56a,b and 60a-d with good prediction activities were synthesized and biologically evaluated. All of these compounds exhibited promising Akt1 inhibitory and antiproliferative activities, suggesting the reliability and good application value of the established MD-SVR model in the development of Akt1 inhibitors.
机译:一组47种Akt1抑制剂通过非线性回归用于开发基于分子对接的QSAR模型。对接分数,关键相互作用谱和分子描述符的集成显着提高了QSAR模型的准确性,并提供了合理的统计参数(Rtrain 2 = 0.948,R test 2 = 0.907和Qcv 2 = 0.794)。进一步进行了建立的基于MD-SVR模型的新的4-氨基-嘧啶衍生物的结构修饰,合成了6种具有良好预测活性的化合物56a,b和60a-d并对其进行了生物学评估。所有这些化合物均显示出有希望的Akt1抑制和抗增殖活性,表明已建立的MD-SVR模型在Akt1抑制剂开发中的可靠性和良好的应用价值。

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