Original article Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays Gerardo M. Casa~ nola-Martı ´n a,b,c , Yovani Marrero-Ponce a,b,d, * , Mahmud Tareq Hassan Khan e,f , Arjumand Ather g , Khalid M. Khan h , Francisco Torrens d , Richard Rotondo i a Unit of Computer-Aided Molecular ‘‘Biosilico’’ Discovery and Bioinformatic Research (CAMD-BIR Unit), Department of Pharmacy, Faculty of ChemistryePharmacy, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba b Department of Drug Design, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba c Department of Biological Sciences, Faculty of Agricultural Sciences, University of Ciego de Avila, 69450 Ciego de Avila, Cuba d Institut Universitari de Cie `ncia Molecular, Universitat de Vale `ncia, Edifici d’Instituts de Paterna, Poligon la Coma s/n (detras de Canal Nou) P.O. Box 22085, E-46071 Valencia, Spain e Pharmacology Research Lab., Faculty of Pharmaceutical Sciences, University of Science and Technology, Chittagong, Bangladesh f Department of Pharmacology, Institute of Medical Biology, University of Tromso, Tromso 9037, Norway g The Norwegian Structural Biology Centre (NorStruct), University of Tromso, Tromso 9037, Norway h HEJ Research Institute of Chemistry, Pakistan i Advanced Medisyns, Inc., 601 Carlson Parkway, Suite 1050, Minnetonka, MN 55305, USA Received 12 September 2006; received in revised form 18 January 2007; accepted 19 January 2007 Available online 23 February 2007 Abstract QSAR (quantitative structureeactivity relationship) studies of tyrosinase inhibitors employing Dragon descriptors and linear discriminant analysis (LDA) are presented here. A data set of 653 compounds, 245 with tyrosinase inhibitory activity and 408 having other clinical uses were used. The active data set was processed by k-means cluster analysis in order 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 Class ¼ 96:067 þ 1:988  10 2 X0Av þ 91:907 BIC3 þ 6:853 CIC1 in the training set. External validation processes to assess the robustness and pre- dictive power of the obtained model were carried out. This external prediction set had an accuracy of 99.44%. After that, the developed models 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 bipiperidine series as new tyrosinase inhibitors. These methods are an adequate alternative to the process of selection/iden- tification of new bioactive compounds. The biosilico assays and in vitro results of inhibitory activity on mushroom tyrosinase showed good cor- respondence. It is important to stand out that compound BP4 (IC 50 ¼ 1.72 mM) showed higher activity in the inhibition against the enzyme than reference compound kojic acid (IC 50 ¼ 16.67 mM) and L-mimosine (IC 50 ¼ 3.68 mM). These results support the role of biosilico algorithm for the identification of new tyrosinase inhibitor compounds. Ó 2007 Elsevier Masson SAS. All rights reserved. Keywords: Dragon descriptor; LDA-based QSAR model; Tyrosinase inhibitor; Bipiperidine series; Virtual screening * Corresponding author. Unit of Computer-Aided Molecular ‘‘Biosilico’’ Discovery and Bioinformatic Research (CAMD-BIR Unit), Department of Pharmacy, Faculty of ChemistryePharmacy, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba. Tel.: þ53 42 281192/473 (Cuba); þ963543156 (Vale `n- cia); fax: þ53 42 281130/455 (Cuba); þ963543156 (Vale `ncia); cell: 610028990. E-mail addresses: ymarrero77@yahoo.es, yovani.marrero@uv.es, ymponce@gmail.com, yovanimp@qf.uclv.edu.cu (Y. Marrero-Ponce). URL: http://www.uv.es/yoma/ 0223-5234/$ - see front matter Ó 2007 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.ejmech.2007.01.026 Available online at www.sciencedirect.com European Journal of Medicinal Chemistry 42 (2007) 1370e1381 http://www.elsevier.com/locate/ejmech