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A novel hybrid method named electron conformational genetic algorithm as a 4D QSAR investigation to calculate the biological activity of the tetrahydrodibenzazosines
Author(s) -
Sahin Kader,
Saripinar Emin
Publication year - 2020
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.26154
Subject(s) - quantitative structure–activity relationship , pharmacophore , algorithm , test set , matrix (chemical analysis) , set (abstract data type) , genetic algorithm , computer science , biological system , chemistry , computational chemistry , artificial intelligence , stereochemistry , machine learning , biology , chromatography , programming language
Abstract To understand the structure–activity correlation of a group of tetrahydrodibenzazocines as inhibitors of 17 β ‐hydroxysteroid dehydrogenase type 3, we have performed a combined genetic algorithm (GA) and four‐dimensional quantitative structure–activity relationship (4D‐QSAR) modeling study. The computed electronic and geometry structure descriptors were regulated as a matrix and named as electron‐conformational matrix of contiguity (ECMC). A chemical property‐based pharmacophore model was developed for series of tetrahydrodibenzazocines by EMRE software package. GA was employed to choose an optimal combination of parameters. A model has been developed for estimating anticancer activity quantitatively. All QSAR models were established with 40 compounds (training set), then they were considered for selective capability with additional nine compounds (test set). A statistically valid 4D‐QSAR ( R training 2 = 0.856 , R test 2 = 0.851 and q 2 = 0.650) with good external set prediction was obtained.