Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors. (1st September 2015)

• Record Type: Journal Article
• Authors: Živković, Jelena V.; Trutić, Nataša V.; Veselinović, Jovana B.; Nikolić, Goran M.; Veselinović, Aleksandar M.
• Is Part Of: Computers in biology and medicine. Volume 64(2015)
• Page Start: 276
• Page End: 282
• Publication Date: 2015-09-01
• Subjects: QSAR -- SMILES -- Monte Carlo method -- Glycogen synthase kinase-3β inhibitors -- CORAL software -- Maleimide derivatives; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.07.004
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The Monte Carlo method was used for QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors. The first QSAR model was developed for a series of 74 3-anilino-4-arylmaleimide derivatives. The second QSAR model was developed for a series of 177 maleimide derivatives. QSAR models were calculated with the representation of the molecular structure by the simplified molecular input-line entry system. Two splits have been examined: one split into the training and test set for the first QSAR model, and one split into the training, test and validation set for the second. The statistical quality of the developed model is very good. The calculated model for 3-anilino-4-arylmaleimide derivatives had following statistical parameters: r 2 =0.8617 for the training set; r 2 =0.8659, and r m 2 =0.7361 for the test set. The calculated model for maleimide derivatives had following statistical parameters: r 2 =0.9435, for the training, r 2 =0.9262 and r m 2 =0.8199 for the test and r 2 =0.8418, r (av)m 2 =0.7469 and ∆ r m 2 =0.1476 for the validation set. Structural indicators considered as molecular fragments responsible for the increase and decrease in the inhibition activity have been defined. The computer-aided design of new potential glycogen synthase kinase-3β inhibitors has been presented by using defined structural alerts. Graphical abstract: Highlights: QSAR models for glycogen synthase kinase-3β inhibitors were built. SMILES notation based optimal descriptors and Monte Carlo method were used. The predictability of proposed QSPR models is very good. Structural alerts with the influence on studied inhibitors are defined.