Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays

Abstract

QSAR (quantitative structure–activity relationship) studies of tyrosinase inhibitors employing Dragon descriptors and linear discriminant analysis (LDA) are presented here. A data set of 653 compounds, 245 with tyrosinase inhibitory activity and 408 having other clinical uses were used. The active data set was processed by k-means cluster analysis in order to design training and prediction series. Seven LDA-based QSAR models were obtained. The discriminant functions applied showed a globally good classification of 99.79% for the best model Class = − 96.067 + 1.988 × 10 2 X0Av + 91.907 BIC3 + 6.853 CIC1 in the training set. External validation processes to assess the robustness and predictive power of the obtained model were carried out. This external prediction set had an accuracy of 99.44%. After that, the developed models were used in ligand-based virtual screening of tyrosinase inhibitors from the literature and never considered in either training or predicting series. In this case, all screened chemicals were correctly classified by the LDA-based QSAR models. As a final point, these fitted models were used in the screening of new bipiperidine series as new tyrosinase inhibitors. These methods are an adequate alternative to the process of selection/identification of new bioactive compounds. The biosilico assays and in vitro results of inhibitory activity on mushroom tyrosinase showed good correspondence. It is important to stand out that compound BP4 (IC 50 = 1.72 μM) showed higher activity in the inhibition against the enzyme than reference compound kojic acid (IC 50 = 16.67 μM) and l-mimosine (IC 50 = 3.68 μM). These results support the role of biosilico algorithm for the identification of new tyrosinase inhibitor compounds.