Application of GA-KPLS and L-M ANN calculations for the prediction of the capacity factor of hazardous psychoactive designer drugs

摘要

The hazardous psychoactive designer drugs are compounds in which part of the molecular structure of a stimulant or narcotic has been modified. Genetic algorithm and kernel partial least square (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between capacity factor (k') and descriptors for 104 hazardous psychoactive designer drugs. These drugs are containing Tryptamine, Phenylethylamine, and Piperazine. The both methods resulted in accurate prediction whereas more accurate results were obtained by L-M ANN model. The best model obtained from L-M ANN showed a good R2 value (determination coefficient between observed and predicted values) for all compounds, which was superior to GA-KPLS models. The stability and prediction ability of these models were validated using leave-group-out cross-validation, external test set, and Y-randomization techniques. This is the first research on the quantitative structure-retention relationship (QSRR) of the designer drugs using the GA-KPLS and L-M ANN.