Feature selection for structure-activity correlation using binary particle swarms.
We present a new feature selection algorithm for structure-activity and structure-property correlation based on particle swarms. Particle swarms explore the search space through a population of individuals that adapt by returning stochastically toward previously successful regions, influenced by the success of their neighbors. This method, which was originally intended for searching multidimensional continuous spaces, is adapted to the problem of feature selection by viewing the location vectors of the particles as probabilities and employing roulette wheel selection to construct candidate subsets. The algorithm is applied in the construction of parsimonious quantitative structure-activity relationship (QSAR) models based on feed-forward neural networks and is tested on three classical data sets from the QSAR literature. It is shown that the method compares favorably with simulated annealing and is able to identify a better and more diverse set of solutions given the same amount of simulation time.
Agrafiotis
Dimitris K
DK
3-Dimensional Pharmaceuticals, Inc., 665 Stockton Drive, Exton, Pennsylvania 19341, USA. dimitris@3dp.com
Cedeño
Walter
W
D000465:Algorithms; D000962:Antimalarials / Q000737:chemistry; D000968:Antimycin A / Q000031:analogs & derivatives* / Q000737:chemistry; D005493:Folic Acid Antagonists / Q000737:chemistry; D008024:Ligands; D016571:Neural Networks, Computer; D011743:Pyrimidines / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D011963:Receptors, GABA-A / Q000737:chemistry
D000962:Antimalarials; D005493:Folic Acid Antagonists; D008024:Ligands; D011743:Pyrimidines; D011963:Receptors, GABA-A; C032456:antimycin; D000968:Antimycin A