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Genetic algorithm for CoMFA setting optimization: 3D‐QSAR study on α‐aminosuberic acid derivatives as anti‐cancer compounds
Author(s) -
Ebrahimi S.,
Azimi G.,
Akhlaghi Y.,
KompanyZareh M.
Publication year - 2013
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2524
Subject(s) - quantitative structure–activity relationship , set (abstract data type) , chemistry , stereochemistry , algorithm , computer science , computational biology , biology , programming language
In most three‐dimensional quantitative structure–activity relationship studies, default SYBYL parameters for comparative molecular field analysis (CoMFA) have been used to derive the models. In this work, a genetic algorithm has been employed for the first time to select the best set of parameters. Three‐dimensional quantitative structure–activity relationship analysis of a set of 33 analogues of α‐aminosuberic acid as a new generation of histone deacetylase inhibitors was performed. Contrary to the ordinary and region focusing CoMFA models, in genetic algorithm optimized model, H‐bond was the preferred field type. Genetic algorithm optimized model showed a better predictive ability ( r 2 pred = 0.982, q 2 LOO = 0.828, and q 2 LMO = 0.795) compared with ordinary ( r 2 pred = 0.937, q 2 LOO = 0.629, and q 2 LMO = 0.537) and region focusing ( r 2 pred = 0.954, q 2 LOO = 0.665, and q 2 LMO = 0.564) models derived by CoMFA default set of parameters. Copyright © 2013 John Wiley & Sons, Ltd.