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首页> 外文期刊>Medicinal chemistry research: an international journal for rapid communications on design and mechanisms of action of biologically active agents >Radial basis function neural networks based on the projection pursuit and principal component analysis approaches: QSAR analysis of fullerene[C60]-based HIV-1 PR inhibitors
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Radial basis function neural networks based on the projection pursuit and principal component analysis approaches: QSAR analysis of fullerene[C60]-based HIV-1 PR inhibitors

机译：基于投影寻踪和主成分分析方法的径向基函数神经网络：基于富勒烯[C60]的HIV-1 PR抑制剂的QSAR分析

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摘要

In this work, projection pursuit (PP) and principal component analysis (PCA) are combined with radial basis function networks (RBFNs) to perform the quantitative structure-activity relationship (QSAR) studies on the binding affinities (pEC(50), i. e., minus decimal logarithm of the 50 % effective concentration) of 47 fullerene derivatives as inhibitors of the human immunodeficiency virus type 1 protease. RBFN is applied to construct the nonlinear QSAR models. The input of RBFN is the scores of PP or PCA, and genetic algorithm is used to select the centers of RBFN. The methods are called PP-GA-RBF and PCA-GA-RBF, respectively. The aim is the performance comparison of the proposed methods. To evaluate the performance of the methods, various statistical parameters such as Q(F2)(2) and r(m)(2) are calculated. The results demonstrated that the predictive performance of the proposed PP-GA-RBF is better than PCA-GA-RBF and previous studies. The applicability domain of the models is assessed by leverage and distance approaches.
机译：在这项工作中，将投影追踪（PP）和主成分分析（PCA）与径向基函数网络（RBFN）结合起来，对结合亲和力（pEC（50），即47种富勒烯衍生物作为人类免疫缺陷病毒1型蛋白酶的抑制剂时，减去50％有效浓度的十进制对数）。 RBFN用于构造非线性QSAR模型。 RBFN的输入是PP或PCA的分数，并使用遗传算法选择RBFN的中心。这些方法分别称为PP-GA-RBF和PCA-GA-RBF。目的是比较所提出方法的性能。为了评估这些方法的性能，计算了各种统计参数，例如Q（F2）（2）和r（m）（2）。结果表明，拟议的PP-GA-RBF的预测性能优于PCA-GA-RBF和先前的研究。模型的适用范围通过杠杆和距离方法进行评估。

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