title: Quantitative structure-activity relationship studies of progesterone receptor binding steroids.
abstract: The selection of appropriate descriptors is an important step in the successful formulation of quantitative structure-activity relationships (QSARs). This paper compares a number of feature selection routines and mapping methods that are in current use. They include forward stepping regression (FSR), genetic function approximation (GFA), generalized simulated annealing (GSA), and genetic neural network (GNN). On the basis of a data set of steroids of known in vitro binding affinity to the progsterone receptor, a number of QSAR models are constructed. A comparison of the predictive qualities for both training and test compounds demonstrates that the GNN protocol achieves the best results among the 2D QSAR that are considered. Analysis of the choice of descriptors by the GNN method shows that the results are consistent with established SARs on this series of compounds.
authors.affiliation: Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, USA.
authors.forename: S S
authors.initials: SS
authors.lastname: So
authors.forename: S P
authors.initials: SP
authors.lastname: van Helden
authors.forename: V J
authors.initials: VJ
authors.lastname: van Geerestein
authors.forename: M
authors.initials: M
authors.lastname: Karplus
mesh_terms: D016571:Neural Networks, Computer; D011485:Protein Binding; D011980:Receptors, Progesterone / Q000378:metabolism*; D013256:Steroids / Q000378:metabolism*; D013329:Structure-Activity Relationship
chemical_list: D011980:Receptors, Progesterone; D013256:Steroids