Atom‐Based 2D Quadratic Indices in Drug Discovery of Novel Tyrosinase Inhibitors: Results of In Silico Studies Supported by Experimental Results

Abstract

Herein we present results of QSAR studies of tyrosinase inhibitors employing one of the atom-based TOMOCOMD-CARDD (acronym of TOpological MOlecular COMputer Design-Computer Aided "Rational" Drug Design) descriptors, molecular quadratic indices, and Linear Discriminant Analysis (LDA) as pattern recognition method. In this way, a database of 246 organic chemicals, reported as tyrosinase inhibitors having great structural variability, was analyzed and presented as a helpful tool, not only for theoretical chemists but also for other researchers in this area. In total, 12 LDA-based QSAR models were obtained, the first six with the non-stochastic total and local quadratic indices and the six remaining models with the stochastic molecular descriptors. The best two models for the non-stochastic and stochastic molecular descriptors, showed an appropriate overall accuracy (92.68 and 89.10%, respectively) and a high Matthews correlation coefficient (C of 0.85 and of 0.84, correspondingly) when applied to the training set. External validation series were also used to validate the obtained models; the 91.67% (C ¼ 0.82) and 90.00% (C ¼ 0.78), were correctly classified, respectively. In order to show the possibilities of the present approach for the ligand-based virtual screening of tyrosinase inhibitors, the developed models were used afterwards in a simulation of a virtual search for tyrosinase inhibitors. For instance, more than 93% (93.33%) and 96% (96.66%) of the screened chemicals were correctly classified by the two best LDA-based QSAR models developed with non-stochastic and stochastic quadratic indices, respectively. Finally, the combination of the obtained models permitted the selection/identification of new diterpenoidal alkaloid leads as tyrosinase inhibitors. The found activity is

Bond-based 2D TOMOCOMD-CARDD approach for drug discovery: aiding decision-making in ‘in silico’ selection of new lead tyrosinase inhibitors

“…On other hand, the computational methods have become in a suitable alternative to the drug design, and have recently applied to QSAR studies of tyrosinase inhibitors [9][10][11], using congeneric or heterogeneous dataset of compounds. In this sense QSAR methods can reduce the costly failures of drug candidates in clinical trials by filtering virtual libraries of chemicals.…”