Patrinos, Panagiotis and Alexandridis, Alex and Afantitis, Antreas and Sarimveis, Haralambos and Igglesi-Markopoulou, Olga Development of nonlinear quantitative structure-activity relationships using RBF networks and evolutionary computing. In: European Symposium on Computer Aided Process Engineering, ESCAPE14, 16th-19th May 2004, Lisbon, Portugal (Unpublished) (2004)Full text not available from this repository.
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.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||Radial Basis Functions, QSAR, Neural Networks, Evolutionary Computing, Genetic Algorithms, Simulating Annealing|
|Subjects:||Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Computer software
|Research Area:||Computer Science and Applications|
|Depositing User:||Ms T. Iannizzi|
|Date Deposited:||06 Dec 2011 13:27|
|Last Modified:||06 Dec 2011 13:27|
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