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Feature Selection for Structure-Activity Correlation Using Binary Particle Dimitris K. Agrafiotis* and Walter Ceden~o
 

Summary: Feature Selection for Structure-Activity Correlation Using Binary Particle
Swarms
Dimitris K. Agrafiotis* and Walter Ceden~o
3-Dimensional Pharmaceuticals, Inc., 665 Stockton Drive, Exton, Pennsylvania 19341
Received October 10, 2001
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.
I. Introduction
In recent years, there has been an increasing need
for novel data-mining methodologies that can analyze
and interpret large volumes of data. Artificial intel-

  

Source: Agrafiotis, Dimitris K. - Molecular Design and Informatics Group, Johnson & Johnson Pharmaceutical Research and Development

 

Collections: Chemistry; Computer Technologies and Information Sciences