in silico screening of IL-1β production inhibitors using chemometric tools

Abstract

The IL-1β play a major role in inflammatory disorders and IL-1β production inhibitors can be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 46 pyridazine derivatives (inhibitors of IL-1β production) and their activities were investigated by Multiple Linear Regression (MLR) technique Stepwise Regression Method (ES-SWR). The genetic algorithm (GA) has been proposed for improvement of the performance of the MLR modeling by choosing the most relevant descriptors. The results show that eight descriptors are able to describe about 83.70% of the variance in the experimental activity of the molecules in the training set. The physical meaning of the selected descriptors is discussed in detail. Power predictions of the QSAR models developed were evaluated using cross-validation, and validation through an external prediction set. The results showed satisfactory goodness-of-fit, robustness and perfect external predictive performance. The applicability domain was used to define the area of reliable predictions. Furthermore, the in silico screening technique was applied in order to predict the structure and potency of new compounds of this type using the proposed QSAR model.