title: Exploring QSARs of some analgesic compounds by PC-ANN.
abstract: Quantitative structure-activity relationship study was performed to understand analgesic activity for a set of 95 heterogeneous analgesic compounds. This study was performed by using the principal component-artificial neural network modeling method, with application of eigenvalue ranking factor selection procedure. The results obtained by principal component-artificial neural network give advanced regression models with good prediction ability using a relatively low number of principal components. A 0.834 correlation coefficient was obtained using principal component-artificial neural network with six extracted principal components.
authors.affiliation: Faculty of Pharmacy, Al-Quds University, PO Box 20002, Jerusalem, Palestine. deeb2000il@yahoo.com
authors.forename: Omar
authors.initials: O
authors.lastname: Deeb
authors.forename: Mojahed
authors.initials: M
authors.lastname: Drabh
mesh_terms: D000700:Analgesics / Q000737:chemistry*; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
chemical_list: D000700:Analgesics