Events The 10th International Electronic Conference on Synthetic Organic Chemistry

Event submissions

Published

This submission belongs to the session g. Computational Chemistry of the event The 10th International Electronic Conference on Synthetic Organic Chemistry

Published date

30 Nov, 2006

Citation

Gerardo M. Casañola-Martín, Yovani Marrero-Ponce, Mahmud Tareq Hassan Khan, Arjumand Ather, Mukhlis N. Sultankhodzhaev, Francisco Torrens, In silico Discovery of Novel Tyrosinase Inhibitors using Atom Based Linear Indices, in Proceedings of The 10th International Electronic Conference on Synthetic Organic Chemistry, 1 November–30 November 2006, MDPI: Basel, Switzerland, doi: 10.3390/ecsoc-10-01451

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In silico Discovery of Novel Tyrosinase Inhibitors using Atom Based Linear Indices

Gerardo M. Casañola-Martín ^1,2

Yovani Marrero-Ponce ^1,3

Mahmud Tareq Hassan Khan ^4,5

Arjumand Ather ⁵

Mukhlis N. Sultankhodzhaev ⁶

Francisco Torrens ³

1. Unit of Computer-Aided Molecular “Biosilico” Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy and Department of Drug Design, Chemical Bioactive Center. Central University of Las Villas, Santa Clara, 54830, Villa Clara, C

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, Edifici d'Instituts de Paterna, Poligon la Coma s/n (detras de Canal Nou) P. O. Box 22085, E-46071 Valencia, Spain

4. Pharmacology Research Laboratory, Faculty of Pharmaceutical Sciences, University of Science and Technology Chittagong, Chittagong, Bangladesh

5. Department of Biochemistry and Molecular Biology, Center for Biotechnology, University of Ferrara, Via L. Borsari, 46, FE 44100 Ferrara, Italy

6. S. Yunusov Institute of Chemistry of Plant Substances, Academy of Sciences, Uzbekistan, Tashkent

Abstract

In the present report it is presented the use of the atom-based linear indices for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones. In this sense, discriminant models were applied and globally good classifications of 93.51% and 92.46% were observed for non-stochastic and stochastic linear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67% and 89.44%. In addition, these fitted models were used in the screening of new cycloartane compounds isolated from herbal plants. A good behaviour is showed between the theoretical and experimental results. These results provided a useful tool that can be used in the identification of new tyrosinase inhibitor compounds.

Keywords

TOMOCOMD-CARDD Software

Atom-based Linear Indices

LDA-based QSAR Model

Tyrosinase Inhibitor

Cycloartanes

Ligand-based Virtual Screening

Modeling of acetylene pyrolysis under vacuum carburizing conditions of steel in a tubular flow reactor

The Dragon Method in the Computational Identification of Novel Tyrosinase Inhibitors. Results Supported by Experimental Assays