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A self‐adaptive genetic algorithm‐artificial neural network algorithm with leave‐one‐out cross validation for descriptor selection in QSAR study
Author(s) -
Wu Jingheng,
Mei Juan,
Wen Sixiang,
Liao Siyan,
Chen Jincan,
Shen Yong
Publication year - 2010
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.21471
Subject(s) - quantitative structure–activity relationship , artificial neural network , feature selection , cross validation , genetic algorithm , algorithm , computer science , stability (learning theory) , selection (genetic algorithm) , variable (mathematics) , artificial intelligence , mathematics , machine learning , mathematical analysis
Based on the quantitative structure‐activity relationships (QSARs) models developed by artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable‐selection approach with molecule descriptors and helped to improve the back‐propagation training algorithm as well. The cross validation techniques of leave‐one‐out investigated the validity of the generated ANN model and preferable variable combinations derived in the GAs. A self‐adaptive GA‐ANN model was successfully established by using a new estimate function for avoiding over‐fitting phenomenon in ANN training. Compared with the variables selected in two recent QSAR studies that were based on stepwise multiple linear regression (MLR) models, the variables selected in self‐adaptive GA‐ANN model are superior in constructing ANN model, as they revealed a higher cross validation (CV) coefficient ( Q 2 ) and a lower root mean square deviation both in the established model and biological activity prediction. The introduced methods for validation, including leave‐multiple‐out, Y‐randomization, and external validation, proved the superiority of the established GA‐ANN models over MLR models in both stability and predictive power. Self‐adaptive GA‐ANN showed us a prospect of improving QSAR model. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010