Virtual Screening Based on QSAR and Molecular Docking of Possible Inhibitors Targeting Chagas CYP51

Abstract

Chagas is a neglected tropical disease caused by the parasite Trypanosoma cruzi with no effective treatment in all its forms. There is a need to find more effective therapeutic alternatives with reduced toxicity. In this contribution, multiple linear regression models were used to identify the molecular descriptors that best describe the inhibitory activity of 52 fenarimol analogues against Trypanosoma cruzi. The topological, physicochemical, thermodynamic, electronic, and charge descriptors were evaluated to cover a wide range of properties that frequently encode biological activity. A model with high predictive value was obtained based on geometrical descriptors and descriptors encoding hydrophobicity and London dispersion forces as necessary for the inhibition of Trypanosoma cruzi-CYP51. Docking methodology was implemented to evaluate molecular interactions in silico. The virtual screening results in this study can be used for rational design of new analogues with improved activity against Chagas disease.