APPLICATION OF GENETIC FUNCTION APPROXIMATION TO QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS AND QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIPS
APPLICATION OF GENETIC FUNCTION APPROXIMATION TO QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS AND QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIPS
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DOI：
10.1021/ci00020a020
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发表时间：
1994-07-01
期刊：
影响因子：
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通讯作者：
HOPFINGER, AJ
中科院分区：
文献类型：
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作者：
ROGERS, D;HOPFINGER, AJ
遗传函数逼近(GFA)算法为定量构效关系(QSAR)和定量构效关系(QSPR)模型的建立提供了新的途径。用GFA算法取代回归分析允许构建与标准技术竞争或优于标准技术的模型，并使其他技术无法提供的附加信息可用。与大多数其他分析算法不同，GFA为用户提供了多个模型；模型群体是通过使用遗传算法进化随机初始模型来创建的。GFA不仅可以使用线性多项式，还可以使用高阶多项式、样条线和高斯函数来建立模型。通过使用基于样条线的项，GFA可以执行一种形式的自动异常值去除和分类。GFA算法已经被应用于三个已公布的数据集，以证明它是一种同时进行QSAR和QSPR的有效工具。
The genetic function approximation (GFA) algorithm offers a new approach to the problem of building quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models. Replacing regression analysis with the GFA algorithm allows the construction of models competitive with, or superior to, standard techniques and makes available additional information not provided by other techniques. Unlike most other analysis algorithms, GFA provides the user with multiple models; the populations of models are created by evolving random initial models using a genetic algorithm. GFA can build models using not only linear polynomials but also higher-order polynomials, splines, and Gaussians. By using spline-based terms, GFA can perform a form of automatic outlier removal and classification. The GFA algorithm has been applied to three published data sets to demonstrate it is an effective tool for doing both QSAR and QSPR.