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ANN‐QSAR Model of Drug‐binding to Human Serum Albumin
Author(s) -
Deeb Omar,
Hemmateenejad Bahram
Publication year - 2007
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/j.1747-0285.2007.00528.x
Subject(s) - principal component analysis , quantitative structure–activity relationship , artificial neural network , ranking (information retrieval) , principal component regression , bovine serum albumin , serum albumin , human serum albumin , biological system , artificial intelligence , drug , chemistry , computational biology , computer science , chromatography , stereochemistry , biology , biochemistry , pharmacology
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.