title: Supervised self-organizing maps in drug discovery. 2. Improvements in descriptor selection and model validation.
abstract: The modeling of nonlinear descriptor-target relationships is a topic of considerable interest in drug discovery. We, herein, continue reporting the use of the self-organizing map-a nonlinear, topology-preserving pattern recognition technique that exhibits considerable promise in modeling and decoding these relationships. Since simulated annealing is an efficient tool for solving optimization problems, we combined the supervised self-organizing map with simulated annealing to build high-quality, highly predictive quantitative structure-activity/property relationship models. This technique was applied to six data sets representing a variety of biological endpoints. Since a high statistical correlation in the training set does not indicate a highly predictive model, the quality of all the models was confirmed by withholding a portion of each data set for external validation. Finally, we introduce new cross-validation and dynamic partitioning techniques to address model overfitting and assessment.
authors.affiliation: Molecular Design Group, Targacept Inc., Winston-Salem, North Carolina 27101-4165, USA. yun-de.xiao@targacept.com
authors.forename: Yun-De
authors.initials: YD
authors.lastname: Xiao
authors.forename: Rebecca
authors.initials: R
authors.lastname: Harris
authors.forename: Ersin
authors.initials: E
authors.lastname: Bayram
authors.forename: Peter Santago
authors.initials: PS
authors.lastname: Ii
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Schmitt
mesh_terms: D000465:Algorithms; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D008954:Models, Biological; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results