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.
Drug-likeness filtering, molecular docking, QSAR modeling, similarity searching, tyrosinase inhibitor, virtual
School of Medicine and Pharmacy, Vietnam National University, Hanoi (VNU) 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam.