Monte Carlo Method‐Based QSAR Modeling of Penicillins Binding to Human Serum Proteins. Issue 1 (18th November 2014)

• Record Type: Journal Article
• Authors: Veselinović, Jovana B.; Toropov, Andrey A.; Toropova, Alla P.; Nikolić, Goran M.; Veselinović, Aleksandar M.
• Is Part Of: Archiv der Pharmazie. Volume 348:Issue 1(2015:Jan.)
• Page Start: 62
• Page End: 67
• Publication Date: 2014-11-18
• Subjects: Pharmaceutical chemistry -- Periodicals; Pharmacology -- Periodicals; 615.19
• DOI: 10.1002/ardp.201400259
• Languages: English
• ISSNs: 0365-6233

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="ardp201400259-sec-0001" sec-type="section"> <p>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: <italic>r</italic><sup>2</sup> = 0.8760, <italic>q</italic><sup>2</sup> = 0.8665, <italic>s</italic> = 8.94 for the training set and <italic>r</italic><sup>2</sup> = 0.9812, <italic>q</italic><sup>2</sup> = 0.9753, <italic>s</italic> = 7.31 for the test set. For the validation set, the statistical parameters were <italic>r</italic><sup>2</sup> = 0.727 and <italic>s</italic> = 12.52, but after removing the three worst outliers, the statistical parameters improved to <italic>r</italic><sup>2</sup> = 0.921 and <italic>s</italic> = 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.</p> </sec> </abstract>