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.
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