title: Does rational selection of training and test sets improve the outcome of QSAR modeling?
abstract: Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.
authors.affiliation: Sustainable Technology Division, National Risk Management Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, USA. martin.todd@epa.gov
authors.forename: Todd M
authors.initials: TM
authors.lastname: Martin
authors.forename: Paul
authors.initials: P
authors.lastname: Harten
authors.forename: Douglas M
authors.initials: DM
authors.lastname: Young
authors.forename: Eugene N
authors.initials: EN
authors.lastname: Muratov
authors.forename: Alexander
authors.initials: A
authors.lastname: Golbraikh
authors.forename: Hao
authors.initials: H
authors.lastname: Zhu
authors.forename: Alexander
authors.initials: A
authors.lastname: Tropsha
mesh_terms: D000465:Algorithms; D000818:Animals; D001688:Biological Products / Q000737:chemistry* / Q000494:pharmacology; D003530:Cyprinidae / Q000254:growth & development; D016208:Databases, Factual; D055808:Drug Discovery; D020128:Inhibitory Concentration 50; D007928:Lethal Dose 50; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship; D051381:Rats; D015203:Reproducibility of Results; D013769:Tetrahymena pyriformis / Q000187:drug effects / Q000254:growth & development; D054928:Validation Studies as Topic
chemical_list: D001688:Biological Products