A Self-Adaptive Genetic Algorithm-Artificial Neural Network Algorithm With Leave-One-Out Cross Validation for Descriptor Selection in QSAR Study

作者：Wu, Jingheng; Mei, Juan; Wen, Sixiang; Liao, Siyan; Chen, Jincan; Shen, Yong^*

来源：Journal of Computational Chemistry, 2010, 31(10): 1956-1968.

DOI：10.1002/jcc.21471

• quantitative structure-activity relationship
• genetic algorithms
• artificial neural network
• leave-one-out
• leave-multiple-out
• multiple linear regression
• Y-randomization

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.

• 出版日期2010-7-30
• 单位中山大学