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Cells 2018, 7(2), 13; https://doi.org/10.3390/cells7020013

Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors

Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, Diag. 113 y 64, C.C. 16, Sucursal 4, La Plata 1900, Argentina
Received: 27 November 2017 / Revised: 23 January 2018 / Accepted: 2 February 2018 / Published: 14 February 2018
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Abstract

A 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
Keywords: polo-like kinase-1 inhibitors; quantitative structure-activity relationships; half-maximal inhibitory concentration; replacement method; molecular descriptors polo-like kinase-1 inhibitors; quantitative structure-activity relationships; half-maximal inhibitory concentration; replacement method; molecular descriptors
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Duchowicz, P.R. Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors. Cells 2018, 7, 13.

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