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The Dragon Method in the Computational Identification of Novel Tyrosinase Inhibitors. Results Supported by Experimental Assays
* 1, 2 , 1, 3 , 4, 5 , 6 , 7 , 8 , 3
1  Unit of Computer-Aided Molecular “Biosilico” Discovery and Bioinformatic Research (CAMD-BIR Unit), Department of Pharmacy, Faculty of Chemistry-Pharmacy and Department of Drug Design, Chemical Bioactive Center. Central University of Las Villas, Santa Clar
2  Department of Biological Sciences, Faculty of Agricultural Sciences, University of Ciego de Avila, 69450, Ciego de Avila, Cuba
3  Institut Universitari de Ciència Molecular, Universitat de València, Dr. Moliner 50, E-46100 Burjassot (València), Spain
4  Pharmacology Research Lab., Faculty of Pharmaceutical Sciences, University of Science and Technology, Chittagong, Bangladesh
5  Department of Pharmacology, Institute of Medical Biology, University of Tromso, Tromso 9037, Norway
6  The Norwegian Structural Biology Centre (NorStruct), University of Tromso, Tromso 9037, Norway
7  HEJ Research Institute of Chemistry, Pakistan
8  Mediscovery, Inc. Suite 1050, 601 Carlson Parkway, Minnetonka, MN 55305, USA

Abstract: QSAR (quantitative structure-activity relationship) studies of tyrosinase inhibitors employing Dragons descriptors and linear discriminant analysis (LDA) are presented here. A dataset of 653 compounds, 245 with tyrosinase inhibitory activity and 408 having other clinical uses were used. The active dataset was processed by k-means cluster analysis 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 (Eq. 3) in the training set. External validation processes to assess the robustness and predictive power of the obtained model was carried out. This external prediction set had an accuracy of 99.44%. After that, the developed 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 bipiperidines series as new tyrosinase inhibitors. The biosilico assays and in vitro results of inhibitory activity on mushroom tyrosinase showed a good correspondence. These results support the role of biosilico algorithm for the identification of new tyrosinase inhibitors compounds.
Keywords: Dragon descriptor, LDA-Based QSAR Model, Tyrosinase Inhibitor, Bipiperidine Series, Virtual Screening