QSAR modeling of PTP1B inhibitor by using Genetic algorithm-Neural network methods

Type-2 diabetes mellitus is an epidemic disease that is characterized by the chronic increase of glucose level. The insulin hormone is known to correspond to this disease, while PTP1B involved in the regulation of this hormone. Hence, PTP1B has become the primary target of drug development to treat this disease. In this study, we aim to develop QSAR model to predict PTP1B inhibitor by using a neural network method. Genetic algorithm (GA) method was used to select the set of the molecular descriptor. We improved the performance of the models by performing a hyperparameter tuning procedure. From the results of validation analysis, we found that the model 2 containing 5 descriptors as the best model. This confirms by the value of MCC (0.68) and AUC (0.89) of this model that is higher than the others. Also, the additional y-scrambled analysis confirms that this model does not correspond to coincidental correlation, indicated by a very low value of MCC of the scrambled model.