title: Predicting allergic contact dermatitis: a hierarchical structure-activity relationship (SAR) approach to chemical classification using topological and quantum chemical descriptors.
abstract: A hierarchical classification study was carried out based on a set of 70 chemicals-35 which produce allergic contact dermatitis (ACD) and 35 which do not. This approach was implemented using a regular ridge regression computer code, followed by conversion of regression output to binary data values. The hierarchical descriptor classes used in the modeling include topostructural (TS), topochemical (TC), and quantum chemical (QC), all of which are based solely on chemical structure. The concordance, sensitivity, and specificity are reported. The model based on the TC descriptors was found to be the best, while the TS model was extremely poor.
authors.affiliation: Natural Resources Research Institute, Center for Water and Environment, University of Minnesota, Duluth, 5013 Miller Trunk Hwy, Duluth, MN, 55811, USA. sbasak@nrri.umn.edu
authors.forename: Subhash C
authors.initials: SC
authors.lastname: Basak
authors.forename: Denise
authors.initials: D
authors.lastname: Mills
authors.forename: Douglas M
authors.initials: DM
authors.lastname: Hawkins
mesh_terms: D003877:Dermatitis, Contact; D006801:Humans; D011789:Quantum Theory; D012680:Sensitivity and Specificity; D013329:Structure-Activity Relationship