title: SMILES-based optimal descriptors: QSAR analysis of fullerene-based HIV-1 PR inhibitors by means of balance of correlations.
abstract: Quantitative structure-activity relationships (QSAR) for prediction of binding affinities (pEC50, i.e., minus decimal logarithm of the 50% effective concentration) of 20 fullerene derivatives inhibitors of the HIV-1 PR (human immunodeficiency virus type 1 protease) have been developed by application of the optimal descriptors approach calculated with SMILES (simplified molecular input line entry system). The applied models were constructed by the balance of correlations. Three various splits of the experimental data into subtraining set, calibration set, and test set were examined. Comparison of classic scheme (training-test system) and the balance of correlations (subtraining-calibration-test system) show that the balance of correlations gives more robust predictions than the classic scheme for the pEC50 of the fullerene derivatives.
authors.affiliation: Institute of Geology and Geophysics, Laboratory of Physicochemical Methods of Analysis, Khodzhibaev St. 49, 100041 Tashkent, Uzbekistan. aatoropov@yahoo.com
authors.forename: Andrey A
authors.initials: AA
authors.lastname: Toropov
authors.forename: Alla P
authors.initials: AP
authors.lastname: Toropova
authors.forename: Emilio
authors.initials: E
authors.lastname: Benfenati
authors.forename: Danuta
authors.initials: D
authors.lastname: Leszczynska
authors.forename: Jerzy
authors.initials: J
authors.lastname: Leszczynski
mesh_terms: D001665:Binding Sites; D002138:Calibration; D037741:Fullerenes / Q000737:chemistry* / Q000494:pharmacology; D016333:HIV Protease / Q000378:metabolism*; D017320:HIV Protease Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D015497:HIV-1 / Q000201:enzymology*; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D021281:Quantitative Structure-Activity Relationship
chemical_list: D037741:Fullerenes; D017320:HIV Protease Inhibitors; D016333:HIV Protease