The use of ladder particle swarm optimisation for quantitative structure–activity relationship analysis of human immunodeficiency virus-1 integrase inhibitors

Abstract

This contribution focuses on the use of ladder particle swarm optimisation (LPSO) on modelling of oxadiazole- and triazole-substituted naphthyridines as human immunodeficiency virus-1 integrase inhibitors. Artificial neural network (ANN) and Monte Carlo cross-validation techniques were combined with LPSO to develop a quantitative structure–activity relationship model. The techniques of LPSO, ANN and sample set partitioning based on joint x–y distances were applied as feature selection, mapping and model evaluation, respectively. The variables selected by LPSO were used as inputs of Bayesian regularisation ANN. The statistical parameters of correlation of deterministic, R ², and root-mean-square error for the test set were 0.876 and 0.23, respectively. Robustness of the model was confirmed by Y-randomisation method. Comparison of the LPSO–ANN results with those of stepwise multiple linear regression (MLR), LPSO–MLR and LPSO–MLR–ANN showed the superiority of LPSO–ANN. Inspection of the selected variables indicated that atomic mass, molecular size and electronic structure of the molecules play a significant role in inhibitory behaviour of oxadiazole- and triazole-substituted naphthyridines.

Keywords:

• quantitative structure–activity relationship
• ladder particle swarm optimisation
• human immunodeficiency virus integrase inhibitors
• Monte Carlo cross validation
• SPXY