Use of Self-Training Artificial Neural Networks in a QSRR Study of a Diverse Set of Organic Compounds

摘要

For a very diverse set of toxicologically compounds, the gas chromatographic Kovats retention indices have been modeled using chemometric methods. First, a genetic algorithm–multiple linear regression (GA–MLR) model has been obtained using molecular descriptors. Then, 15 selected descriptors in the GA–MLR model have been used as input for a self-training artificial neural network (STANN). STANN has been developed as a faster and more accurate non-linear method in our laboratory. After optimization, a 15-9-1 STANN was generated for prediction of retention indices of these organic compounds. The predictive quality of the STANN model was tested for an external prediction set and also five leave-multiple-outs cross-validation sets. Obtained results showed the ability of developed STANN model for predicting retention indices of various compounds. Also, obtained results indicate that in this QSRR study, genetic algorithm is a suitable method for selecting the molecular descriptors. Keywords Quantitative structure retention relationships - Self-training artificial neural networks - Multiple linear regression - Molecular descriptors - Retention index