ADMET, QSAR and Docking studies to predict the activity of tyrosinase-derived medications inhibitors based on computational techniques

Abstract

The tyrosinase enzyme plays a pivotal role in melanin pigment production; however, heightened tyrosinase activity can lead to undesired pigmentation. Consequently, inhibiting this enzyme's function stands as a critical research avenue for devising effective strategies to mitigate pigmentation issues. This study aimed to forecast the biological activity of chemical compounds capable of inhibiting tyrosinase and elucidate pivotal elements influencing this enzyme's activity. To achieve this goal, we employed computational techniques to construct a model predicting the biological activity of these compounds. Initially, we identified 27 tyrosinase inhibitors from previous studies. Subsequently, after performing ADMET studies, we extracted and pre-processed the significant features of each compound to develop a Stepwise-MLR model. Moreover, with the help of this model, we were able to identify the most influential and novel structural features that directly affect enzyme activity and determine the importance factor of each feature. Furthermore, all derived inhibitors with evaluated inhibition constants were docked to the active site of target tyrosinase to investigate the binding mode of the compounds. Docking analysis indicated T1 as the most stable compound with a binding energy of −8.00 kcal/mol. T1 as the most active compound identified through these computational studies can be applied as a prospective tyrosinase inhibitor. The implications of our findings extend to the development of new therapies for pigmentation disorders, notably within the cosmetic and dermatological sectors.