Summary: Journal of Computer-Aided Molecular Design 17: 255263, 2003.
© 2003 Kluwer Academic Publishers. Printed in the Netherlands.
Using particle swarms for the development of QSAR models based on
K-nearest neighbor and kernel regression
Walter Cedeño & Dimitris K. Agrafiotis
3-Dimensional Pharmaceuticals, Inc., 665 Stockton Drive, Exton, PA 19341, USA.
Received 18 November 2002; Accepted for publication 24 January 2003
Key words: computer-assisted drug design, QSAR, feature selection, feature weighting, particle swarm, simulated
annealing, k-nearest neighbors, kernel regression, optimization
We describe the application of particle swarms for the development of quantitative structure-activity relation-
ship (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.
The increasing amount of information available in di-
gital form has prompted the development of novel data