Genetic algorithms (GA) have been widely used in quantitative structure-activity/property relationship (QSAR/QSPR) modeling in recent years and have been shown to generate accurate and robust predictions. In a GA, a population of chromosomes is evolved through the processes of random mutation and crossover and evaluated using a fitness function. Here, we will review the basic principles underlying GA and provide a survey of recent applications in QSAR/QSPR, bioinformatics, and in silico drug design, with particular emphasis on the use of GAs in feature selection and dimensionality reduction, model optimization, conformational search, docking, and diversity analysis.
CITATION STYLE
Sukumar, N., Prabhu, G., & Saha, P. (2014). Applications of genetic algorithms in QSAR/QSPR modeling. In Applications of Metaheuristics in Process Engineering (Vol. 9783319065083, pp. 315–324). Springer International Publishing. https://doi.org/10.1007/978-3-319-06508-3_13
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