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Monte Carlo Method‐Based QSAR Modeling of Penicillins Binding to Human Serum Proteins
Author(s) -
Veselinović Jovana B.,
Toropov Andrey A.,
Toropova Alla P.,
Nikolić Goran M.,
Veselinović Aleksandar M.
Publication year - 2015
Publication title -
archiv der pharmazie
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.468
H-Index - 61
eISSN - 1521-4184
pISSN - 0365-6233
DOI - 10.1002/ardp.201400259
Subject(s) - quantitative structure–activity relationship , monte carlo method , chemistry , test set , outlier , molecular descriptor , set (abstract data type) , biological system , computational chemistry , stereochemistry , mathematics , computer science , statistics , biology , programming language
The binding of penicillins to human serum proteins was modeled with optimal descriptors based on the Simplified Molecular Input‐Line Entry System (SMILES). The concentrations of protein‐bound drug for 87 penicillins expressed as percentage of the total plasma concentration were used as experimental data. The Monte Carlo method was used as a computational tool to build up the quantitative structure–activity relationship (QSAR) model for penicillins binding to plasma proteins. One random data split into training, test and validation set was examined. The calculated QSAR model had the following statistical parameters: r 2 = 0.8760, q 2 = 0.8665, s = 8.94 for the training set and r 2 = 0.9812, q 2 = 0.9753, s = 7.31 for the test set. For the validation set, the statistical parameters were r 2 = 0.727 and s = 12.52, but after removing the three worst outliers, the statistical parameters improved to r 2 = 0.921 and s = 7.18. SMILES‐based molecular fragments (structural indicators) responsible for the increase and decrease of penicillins binding to plasma proteins were identified. The possibility of using these results for the computer‐aided design of new penicillins with desired binding properties is presented.