title: Interpreting computational neural network QSAR models: a measure of descriptor importance.
abstract: We present a method to measure the relative importance of the descriptors present in a QSAR model developed with a computational neural network (CNN). The approach is based on a sensitivity analysis of the descriptors. We tested the method on three published data sets for which linear and CNN models were previously built. The original work reported interpretations for the linear models, and we compare the results of the new method to the importance of descriptors in the linear models as described by a PLS technique. The results indicate that the proposed method is able to rank descriptors such that important descriptors in the CNN model correspond to the important descriptors in the linear model.
authors.affiliation: Chemistry Department, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
authors.forename: Rajarshi
authors.initials: R
authors.lastname: Guha
authors.forename: Peter C
authors.initials: PC
authors.lastname: Jurs
mesh_terms: D008958:Models, Molecular; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship