Using particle swarms for the development of QSAR models based on K-nearest neighbor and kernel regression.
We describe the application of particle swarms for the development of quantitative structure-activity relationship (QSAR) models based on k-nearest neighbor and kernel regression. Particle swarms is a population-based stochastic search method based on the principles of social interaction. Each individual explores the feature space guided by its previous success and that of its neighbors. Success is measured using leave-one-out (LOO) cross validation on the resulting model as determined by k-nearest neighbor kernel regression. The technique is shown to compare favorably to simulated annealing using three classical data sets from the QSAR literature.
D000465:Algorithms; D001185:Artificial Intelligence; D002965:Classification / Q000379:methods; D003198:Computer Simulation; D008954:Models, Biological; D008956:Models, Chemical; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D011157:Population Dynamics; D021281:Quantitative Structure-Activity Relationship; D013269:Stochastic Processes