New tyrosinase inhibitors selected by atomic linear indices-based classification models.
In the present report, the use of the atom-based linear indices for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones is presented. 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 behavior is shown between the theoretical and experimental results. These results provide a tool that can be used in the identification of new tyrosinase inhibitor compounds.
Casañola-Martín
Gerardo M
GM
Department of Pharmacy, Faculty of Chemistry-Pharmacy, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba.
Khan
Mahmud Tareq Hassan
MT
Marrero-Ponce
Yovani
Y
Ather
Arjumand
A
Sultankhodzhaev
Mukhlis N
MN
Torrens
Francisco
F
D003198:Computer Simulation; D004791:Enzyme Inhibitors / Q000737:chemistry / Q000145:classification / Q000494:pharmacology*; D008956:Models, Chemical; D008968:Molecular Conformation; D014442:Monophenol Monooxygenase / Q000037:antagonists & inhibitors*; D021281:Quantitative Structure-Activity Relationship; D012680:Sensitivity and Specificity; D014315:Triterpenes / Q000737:chemistry / Q000145:classification / Q000494:pharmacology*
D004791:Enzyme Inhibitors; D014315:Triterpenes; C085352:cycloartane; D014442:Monophenol Monooxygenase