title: Ensemble feature selection: consistent descriptor subsets for multiple QSAR models.
abstract: Selecting a small subset of descriptors from a large pool to build a predictive quantitative structure-activity relationship (QSAR) model is an important step in the QSAR modeling process. In general, subset selection is very hard to solve, even approximately, with guaranteed performance bounds. Traditional approaches employ deterministic or stochastic methods to obtain a descriptor subset that leads to an optimal model of a single type (such as linear regression or a neural network). With the development of ensemble modeling approaches, multiple models of differing types are individually developed resulting in different descriptor subsets for each model type. However, it is advantageous, from the point of view of developing interpretable QSAR models, to have a single set of descriptors that can be used for different model types. In this paper, we describe an approach to the selection of a single, optimal, subset of descriptors for multiple model types. We apply this approach to three data sets, covering both regression and classification, and show that the constraint of forcing different model types to use the same set of descriptors does not lead to a significant loss in predictive ability for the individual models considered. In addition, interpretations of the individual models developed using this approach indicate that they encode similar structure-activity trends.
authors.affiliation: School of Informatics, Indiana University, Bloomington, Indiana 47406, USA.
authors.forename: Debojyoti
authors.initials: D
authors.lastname: Dutta
authors.forename: Rajarshi
authors.initials: R
authors.lastname: Guha
authors.forename: David
authors.initials: D
authors.lastname: Wild
authors.forename: Ting
authors.initials: T
authors.lastname: Chen
mesh_terms: D051546:Cyclooxygenase 2 / Q000378:metabolism; D052246:Cyclooxygenase 2 Inhibitors / Q000737:chemistry / Q000494:pharmacology; D016208:Databases, Factual; D065427:Factor Xa Inhibitors; D016014:Linear Models; D008956:Models, Chemical; D017711:Nonlinear Dynamics; D010982:Platelet-Derived Growth Factor / Q000037:antagonists & inhibitors; D021281:Quantitative Structure-Activity Relationship
chemical_list: D052246:Cyclooxygenase 2 Inhibitors; D065427:Factor Xa Inhibitors; D010982:Platelet-Derived Growth Factor; D051546:Cyclooxygenase 2