Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
AbstractA structurally diverse dataset of 530 polo-like kinase-1 (PLK1) inhibitors is compiled from the ChEMBL database and studied by means of a conformation-independent quantitative structure-activity relationship (QSAR) approach. A large number (26,761) of molecular descriptors are explored with the main intention of capturing the most relevant structural characteristics affecting the bioactivity. The structural descriptors are derived with different freeware, such as PaDEL, Mold2, and QuBiLs-MAS; such descriptor software complements each other and improves the QSAR results. The best multivariable linear regression models are found with the replacement method variable subset selection technique. The balanced subsets method partitions the dataset into training, validation, and test sets. It is found that the proposed linear QSAR model improves previously reported models by leading to a simpler alternative structure-activity relationship. View Full-Text
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Duchowicz, P.R. Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors. Cells 2018, 7, 13.
Duchowicz PR. Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors. Cells. 2018; 7(2):13.Chicago/Turabian Style
Duchowicz, Pablo R. 2018. "Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors." Cells 7, no. 2: 13.