Artificial neural network-based transformation for nonlinear partial least-square regression with application to QSAR studies.
In the present study a new version of nonlinear partial least-square method based on artificial neural network transformation (ANN-NLPLS) has been proposed. This algorithm firstly transforms the training descriptors into the hidden layer outputs using the universal nonlinear mapping carried by an artificial neural network, and then utilizes PLS to relate the outputs of the hidden layer to the bioactivities. The weights between the input and hidden layers are optimized by a particle swarm optimization (PSO) method using the criterion of minimized model error via PLS modeling. An F-statistic is introduced to determine automatically the number of PLS components during the weight optimization. The performance is assessed using a simulated data set and two quantitative structure-activity relation (QSAR) data sets. Results of these three data sets demonstrate that ANN-NLPLS offers enhanced capacity in modeling nonlinearity while circumventing the overfitting frequently involved in nonlinear modeling.
Zhou
Yan-Ping
YP
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, P R.China.
Jiang
Jian-Hui
JH
Lin
Wei-Qi
WQ
Xu
Lu
L
Wu
Hai-Long
HL
Shen
Guo-Li
GL
Yu
Ru-Qin
RQ