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

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...

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Detalles Bibliográficos
Autor principal: Duchowicz, Pablo Román
Formato: Articulo
Lenguaje:Inglés
Publicado: 2018
Materias:
Biología
Polo-like kinase-1 inhibitors
Quantitative structure-activity relationships
Half-maximal inhibitory concentration
Replacement method
Molecular descriptors
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/125375
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-125375
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Biología
Polo-like kinase-1 inhibitors
Quantitative structure-activity relationships
Half-maximal inhibitory concentration
Replacement method
Molecular descriptors
spellingShingle Biología
Polo-like kinase-1 inhibitors
Quantitative structure-activity relationships
Half-maximal inhibitory concentration
Replacement method
Molecular descriptors
Duchowicz, Pablo Román
Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
topic_facet Biología
Polo-like kinase-1 inhibitors
Quantitative structure-activity relationships
Half-maximal inhibitory concentration
Replacement method
Molecular descriptors
description 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.
format Articulo
Articulo
author Duchowicz, Pablo Román
author_facet Duchowicz, Pablo Román
author_sort Duchowicz, Pablo Román
title Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
title_short Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
title_full Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
title_fullStr Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
title_full_unstemmed Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
title_sort linear regression qsar models for polo-like kinase-1 inhibitors
publishDate 2018
url http://sedici.unlp.edu.ar/handle/10915/125375
work_keys_str_mv AT duchowiczpabloroman linearregressionqsarmodelsforpololikekinase1inhibitors
bdutipo_str Repositorios
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