In silico Discovery of Novel Tyrosinase Inhibitors using Atom Based Linear Indices
Abstract
In the present report it is presented the use of the atom-based linear indices for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones. In this sense, discriminant models were applied and globally good classifications of 93.51% and 92.46% were observed for non-stochastic and stochastic linear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67% and 89.44%. In addition, these fitted models were used in the screening of new cycloartane compounds isolated from herbal plants. A good behaviour is showed between the theoretical and experimental results. These results provided a useful tool that can be used in the identification of new tyrosinase inhibitor compounds.