Abstract

The tyrosinase is a bifunctional, copper-containing enzyme widely distributed in the phylogenetic tree. This enzyme is involved in the production of melanin and some other pigments in humans, animals and plants, including skin pigmentations in mammals, and browning process in plants and vegetables. Therefore, enzyme inhibitors has been under the attention of the scientist community, due to its broad applications in food, cosmetic, agricultural and medicinal fields, to avoid the undesirable effects of abnormal melanin overproduction. However, the research of novel chemical with antityrosinase activity demands the use of more efficient tools to speed up the tyrosinase inhibitors discovery process. This chapter is focused in the different components of a predictive modeling workflow for the identification and prioritization of potential new compounds with activity against the tyrosinase enzyme. In this case, two structure chemical libraries Spectrum Collection and Drugbank are used in this attempt to combine different virtual screening data mining techniques, in a sequential manner helping to avoid the usually expensive and time consuming traditional methods. Some of the sequential steps summarize here comprise the use of drug-likeness filters, similarity searching, classification and potency QSAR multiclassifier systems, modeling molecular interactions systems, and similarity/diversity analysis. Finally, the methodologies showed here provide a rational workflow for virtual screening hit analysis and selection as a promissory drug discovery strategy for use in target identification phase.

Keywords: Drug-likeness filtering, molecular docking, QSAR modeling, similarity searching, tyrosinase inhibitor, virtual screening.

Graphical Abstract