relation: http://eprints.imtlucca.it/1039/
title: Development of nonlinear quantitative structure-activity relationships using RBF networks and evolutionary computing
creator: Patrinos, Panagiotis
creator: Alexandridis, Alex
creator: Afantitis, Antreas
creator: Sarimveis, Haralambos
creator: Igglesi-Markopoulou, Olga
subject: QA Mathematics
subject: QA76 Computer software
description: 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.
date: 2004
type: Conference or Workshop Item
type: PeerReviewed
identifier:   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  In: European Symposium on Computer Aided Process Engineering, ESCAPE14, 16th-19th May 2004, Lisbon, Portugal       (Unpublished)   (2004)  
relation: http://www.deb.uminho.pt/escape14/PDFs/PPDp011.pdf