title: ANN-QSAR model of drug-binding to human serum albumin.
abstract: Quantitative structure-activity relationship study was performed to understand drug binding to human serum albumin. This study was performed on 94 different human serum albumin drug and drug-like compounds 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 gives better regression models with good prediction ability using a relatively low number of principal components in comparison to other quantitative structure-activity relationship studies on the same data set of compounds. A 0.8497 coefficient of determination 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: Bahram
authors.initials: B
authors.lastname: Hemmateenejad
mesh_terms: D006801:Humans; D008956:Models, Chemical; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000378:metabolism*; D025341:Principal Component Analysis; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D015203:Reproducibility of Results; D012709:Serum Albumin / Q000737:chemistry / Q000378:metabolism*; D012984:Software
chemical_list: D004364:Pharmaceutical Preparations; D012709:Serum Albumin