%0 Conference Paper
%A Patrinos, Panagiotis
%A Alexandridis, Alex
%A Afantitis, Antreas
%A Sarimveis, Haralambos
%A Igglesi-Markopoulou, Olga
%B European Symposium on Computer Aided Process Engineering, ESCAPE14
%C Lisbon, Portugal
%D 2004
%F eprints:1039
%K Radial Basis Functions, QSAR, Neural Networks, Evolutionary Computing, Genetic Algorithms, Simulating Annealing
%T Development of nonlinear quantitative structure-activity relationships using RBF networks and evolutionary computing
%U http://eprints.imtlucca.it/1039/
%X Quantitative Structure Activity Relationships (QSARs) are mathematical models that correlate structural or property descriptions of compounds (hydrophobicity, topology, electronic properties etc.) with activities, such as chemical measurements and biological assays. In this paper we propose a modeling methodology suitable for QSAR studies which selects the proper descriptors based on evolutionary computing and finally produces Radial Basis Function (RBF) neural network models. The method is successfully applied to the benchmark Selwood data set.