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QSAR Models Guided by Molecular Dynamics Applied to Human Glucokinase Activators
Author(s) -
Assis Tamiris Maria,
Gajo Giovanna Cardoso,
Assis Letícia Cristina,
Garcia Letícia Santos,
Silva Daniela Rodrigues,
Ramalho Teodorico Castro,
Cunha Elaine Fontes Ferreira
Publication year - 2016
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/cbdd.12683
Subject(s) - glucokinase , quantitative structure–activity relationship , molecular dynamics , computational biology , set (abstract data type) , partial least squares regression , chemistry , biological system , computer science , biology , computational chemistry , biochemistry , gene , machine learning , stereochemistry , programming language
In this study, quantitative structure–activity relationship studies which make use of molecular dynamics trajectories were performed on a set of 54 glucokinase protein activators. The conformations obtained by molecular dynamics simulation were superimposed according to the twelve alignments tested in a virtual three‐dimensional box comprised of 2 Å cells. The models were generated by the technique that combines genetic algorithms and partial least squares. The best alignment models generated with a determination coefficient ( r 2 ) between 0.674 and 0.743 and cross‐validation ( q 2 ) between 0.509 and 0.610, indicating good predictive capacity. The 4D‐ QSAR models developed in this study suggest novel molecular regions to be explored in the search for better glucokinase activators.