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Bond-Based 2D Quadratic Fingerprints in QSAR Studies. Virtual and In Vitro Tyrosinase Inhibitory Activity Elucidation
* 1, 2, 3 , 1, 4 , 5, 6 , 7 , 8 , 9 , 2 , 10 , 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  Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, Polígon la Coma s/n P. O. Box 22085, E-46071 Valencia, Spain
3  Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, València, Spain
4  Department of Biological Sciences, Faculty of Agricultural Sciences, University of Ciego de Avila, 69450, Ciego de Avila, Cuba
5  Pharmacology Research Lab., Faculty of Pharmaceutical Sciences, University of Science and Technology, Chittagong, Bangladesh
6  Department of Pharmacology, Institute of Medical Biology, University of Tromso, Tromso 9037, Norway
7  The Norwegian Structural Biology Centre (NorStruct), University of Tromso, Tromso 9037, Norway
8  HEJ Research Institute of Chemistry, University of Karachi, Pakistan
9  Faculty of Informatics, University of Cienfuegos, Cienfuegos 59430, Cuba
10  Advanced Medisyns, Inc. 601 Carlson Parkway, Suite 1050, Minnetonka, Minnesota 55305, USA

Abstract: In this report, we show the results of QSAR (quantitative structure-activity relationship) studies of tyrosinase inhibitory activity, by using the bond-based quadratic indices as molecular descriptors (MDs) and linear discriminant analysis (LDA), togenerate discriminant functions to predict the anti-tyrosinase activity. The best two models (Eqs.8 and 14) of the total 12 QSAR models developed here show accuracies of 93.51% and 91.21%, as well as high Matthews correlation coefficients (C) of 0.86 and 0.82, respectively, in the training set. The validation external series depicts values of 90.00% and 89.44% for these best two equations 8 and 14, correspondingly. Afterwards, a second external prediction data was used, to perform a virtual screening of compounds reported in the literature as active (tyrosinase inhibitors). In a final step, a series of lignans is analyzed by using the in silico developed models, and in vitro corroboration of the activity is carried out. An aspect of great importance to remark here, is that all compounds present greater inhibition values than Kojic Acid (standard tyrosinase inhibitor: IC50 =16.67µM). The current obtained results could be used as a method to increase the speed, in the biosilico discovery of leads for the treatment of skin disorders.
Keywords: TOMOCOMD-CARDD Software, Non-Stochastic and StochasticBond-Based Quadratic Indices, LDA-Based QSAR Model, Tyrosinase Inhibitor, Virtual Screening, Lignan