In silico modelling of azole derivatives with tyrosinase inhibition ability: Application of the models for activity prediction of new compounds. (June 2018) - De, Biplab; Adhikari, Indrani; Nandy, Ashis; Saha, Achintya; Goswami, Binoy Behari
Graphical abstract: Highlights: Molecular modelling of thiazolidine derivatives with tyrosinase inhibitory activity. Quantitaive analysis of the essential molecular fragments based on QSAR analysis. Identification of the biological pharmacophore responsible for the activity of the molecules. Screening of in-house synthesised antioxidant molecules to assess their ability to inhibit tyrosinase enzyme using the developed in silico models. Identification of new molecules with tyrosinase inhibition ability based on in silico prediction methodology. Abstract: Tyrosinase is a metal containing multifunctional enzymes found in animals, fruits and vegetables and constitutes the primary cause for diseases resulting from overproduction of melanin as well as for browning of fruits. Inhibitors of the enzyme have thus gained increased importance in food and cosmetic industry. In the present work, a group of azole derivatives with tyrosinase inhibitory activity were explored to analyse the prime structural attributes of the potent inhibitors. In silico models have been developed in order to have a close insight regarding features of the molecular fragments that may affect the activity of the molecules conducively. The biological pharmacophore of the inhibitors that accounts for their interaction with the tyrosinase enzyme has been ascertained based on the development of a 3D pharmacophore model. The models thus developed were subsequently utilised for screening a set of compounds that were previously synthesised in-house and were reported to possess antioxidant activity. The final selection of active molecules in the screening process was done based on the docking interactions of the molecules with the tyrosinase enzyme and assessment of their degree of binding to the protein. Thus the developed models have been successfully utilised for identifying active compounds from a series of untested molecules.