[Bayesian regularized BP neural network model for quantitative relationship between the electrochemical reduction potential and molecular structures of chlorinated aromatic compounds].
Bayesian regularized BP neural network (BRBPNN) technique was applied in QSPR model in environmental field. The BRBPNN model for quantitative relationship between the electrochemical reduction potential (ERP) and chemical structures of 87 chlorinated aromatic compounds was established. The structure descriptor pool is consisted of Cl number (Cl), molecular weight (MW) and 6 quantum chemistry parameters which are calculated by MOPAC2000 built in ChemOffice2004, including energy of the highest occupied molecular orbital (E(HOMO)), energy of the lowest occupied molecular orbital (E(LUMO)), heat of formation(HF), dipole(DIP), electronic energy(EE), core-core repulsion(CCR). The achieved optimal network structure was 6-20-1, which possessed stronger fitting and prediction capacity than that of the stepwise linear regression and with the correlation coefficients square and the mean square error for the training set and the test set as 0.999 and 0.000105, 0.965 and 0.00159 respectively. The sum of square weights between each input neuron and the hidden layer of BRBPNN(6-20-1) indicate the effect of descriptor on the electric potential declining in the order of ELUMO > EHOMO > HF> CCR > EE > DIP. The scatter diagrams show that the EE descriptors had positive effect on ERP, and ELUMO, HF, DIP had negative effects, and EHOMO and CCR showed ambiguous effects. Results show that Bayesian regularized BP neural network is of automated regularization parameter selection capability and thus may ensure the excellent generation ability and robustness. This study threw more light on the applicability of electrochemical treatment for the chlorinated aromatic compounds and the analysis on electrochemical reduction mechanism.
Sun
Wei
W
Department of Environmental Science & Engineering, Hunan University, Changsha 410082, China.
Zeng
Guang-ming
GM
Wei
Wan-zhi
WZ
Huang
Guo-he
GH
D001499:Bayes Theorem; D004563:Electrochemistry; D006843:Hydrocarbons, Chlorinated / Q000737:chemistry*; D008956:Models, Chemical; D015394:Molecular Structure; D008970:Molecular Weight; D016571:Neural Networks, Computer
D006843:Hydrocarbons, Chlorinated