Details

Autor(en) / Beteiligte

• Bazl, Roya
• Ganjali, Mohammad Reza
• Derakhshankhah, Hossein
• Saboury, Ali Akbar
• Amanlou, Massoud
• Norouzi, Parviz

Titel

Prediction of tyrosinase inhibition for drug design using the genetic algorithm–multiple linear regressions

Ist Teil von

• Medicinal chemistry research, 2013-11, Vol.22 (11), p.5453-5465

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2013

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Modeling inhibition data for a series of most potent tyrosinase inhibitors for hyper-pigmentation treatment was done with quantitative structure–activity relationship (QSAR). In this report, multiple linear regression (MLR) methodology coupled with feature selection method, genetic algorithm (GA), was applied to derive QSAR models. A model with seven selected descriptors was obtained by the database that consisted of 49 compounds. The power of the model for prediction was verified with the leave-one-out and leave-group-out cross-validation test, which have values of 0.766 and 0.795, respectively. Moreover, the statistical parameters provided by the GA–MLR model ( R train 2  = 0.85, R test 2  = 0.84, and F train 2  = 24.498) lead to a better understanding of the structural requirements of designing novel potent tyrosinase inhibitors. The study showed that the number of six-membered rings, the branching of a molecule, the van der Waals volume of atoms, the lopping centric index, the global charge transfer in a molecule and atomic masses are the main criteria in the inhibitory activity of compounds.