Development of prediction model for fructose- 1,6- bisphosphatase inhibitors using the Monte Carlo method

ABSTRACT

Fructose-1,6-bisphosphatase (FBPase) is an enzyme important for regulation of gluconeogenesis, which is a major process in the liver responsible for glucose production. Inhibition of FBPase enzyme causing blockage of the gluconeogenesis process represents a newer scheme in the progress of anti-diabetic drugs. The current research describes the development of hybrid optimal descriptors-based quantitative structure–activity relationship (QSAR) models intended for a set of 62 FBPase inhibitors with the Monte Carlo method. The molecular structures were expressed by the simplified molecular input line entry system (SMILES) notation. Three splits were prepared by random division of the molecules into training set, calibration set and validation set. Statistical parameters obtained from QSAR modelling were good for various designed splits. The best QSAR model showed the following parameters: the values of r² for calibration set and validation set of the best model were 0.6837 and 0.8623 and of Q² were 0.6114 and 0.8036, respectively. Based on the results obtained for correlation weights, different structural attributes were described as promoter of the endpoint. Further, these structural attributes were used in designing of new FBPase inhibitors and a molecular docking study was completed for the determination of interactions of the designed molecules with the enzyme.

KEYWORDS:

• CORAL
• QSAR
• FBPase
• SMILES
• docking

Acknowledgements

The authors are highly indebted to Dr Andrey A. Toropov and Dr Alla P. Toropova for providing the CORAL software. We are also grateful to A. Pedretti, Dipartimento di Scienze Farmaceutiche, Facoltà di Scienze del Farmaco, Università degli Studi di Milano, Italy for providing the VEGA ZZ software. We would also like to thank the Drug Theoretics and Cheminformatics (DTC) Laboratory, Jadavpur University for providing the MLRPlus Validation Tool for external validation.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental Material

Supplementary material for this article can be accessed at: https://doi.org/10.1080/1062936X.2019.1568299