Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships
1994
Rogers, D. | Hopfinger, A.J.
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.
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