Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors

Obesity is one of the most provoking health burdens in the developed countries. One of the strategies to prevent obesity is the inhibition of pancreatic lipase enzyme. The aim of this study was to build QSAR models for natural lipase inhibitors by using the Monte Carlo method. The molecular structures were represented by the simplified molecular input line entry system (SMILES) notation and molecular graphs. Three sets - training, calibration and test set of three splits were examined and validated. Statistical quality of all the described models was very good. The best QSAR model showed the following statistical parameters: r(2) = 0.864 and Q(2) = 0.836 for the test set and r(2) = 0.824 and Q(2) = 0.819 for the validation set. Structural attributes for increasing and decreasing the activity (expressed as piC(50)) were also defined. Using defined structural attributes, the design of new potential lipase inhibitors is also presented. Additionally, a molecular docking study was performed for the determination of binding modes of designed molecules.