Monte Carlo method-based QSAR modeling of penicillins binding to human serum proteins

Jovana B. Veselinović, Andrey A. Toropov, Alla P. Toropova, Goran M. Nikolić, Aleksandar M. Veselinović

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

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: r2 = 0.8760, q2 = 0.8665, s = 8.94 for the training set and r2 = 0.9812, q2 = 0.9753, s = 7.31 for the test set. For the validation set, the statistical parameters were r2 = 0.727 and s = 12.52, but after removing the three worst outliers, the statistical parameters improved to r2 = 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.

Original languageEnglish
Pages (from-to)62-67
Number of pages6
JournalArchiv der Pharmazie
Volume348
Issue number1
DOIs
Publication statusPublished - 2015

Fingerprint

Monte Carlo Method
Quantitative Structure-Activity Relationship
Penicillins
Blood Proteins
Computer-Aided Design
somatotropin-binding protein
Pharmaceutical Preparations
Proteins

Keywords

  • CORAL software
  • Monte Carlo method
  • Penicillins
  • QSAR
  • SMILES

ASJC Scopus subject areas

  • Drug Discovery
  • Pharmaceutical Science
  • Medicine(all)

Cite this

Veselinović, J. B., Toropov, A. A., Toropova, A. P., Nikolić, G. M., & Veselinović, A. M. (2015). Monte Carlo method-based QSAR modeling of penicillins binding to human serum proteins. Archiv der Pharmazie, 348(1), 62-67. https://doi.org/10.1002/ardp.201400259

Monte Carlo method-based QSAR modeling of penicillins binding to human serum proteins. / Veselinović, Jovana B.; Toropov, Andrey A.; Toropova, Alla P.; Nikolić, Goran M.; Veselinović, Aleksandar M.

In: Archiv der Pharmazie, Vol. 348, No. 1, 2015, p. 62-67.

Research output: Contribution to journalArticle

Veselinović, JB, Toropov, AA, Toropova, AP, Nikolić, GM & Veselinović, AM 2015, 'Monte Carlo method-based QSAR modeling of penicillins binding to human serum proteins', Archiv der Pharmazie, vol. 348, no. 1, pp. 62-67. https://doi.org/10.1002/ardp.201400259
Veselinović, Jovana B. ; Toropov, Andrey A. ; Toropova, Alla P. ; Nikolić, Goran M. ; Veselinović, Aleksandar M. / Monte Carlo method-based QSAR modeling of penicillins binding to human serum proteins. In: Archiv der Pharmazie. 2015 ; Vol. 348, No. 1. pp. 62-67.
@article{c634211f534f45879594fa6e74d9569f,
title = "Monte Carlo method-based QSAR modeling of penicillins binding to human serum proteins",
abstract = "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: r2 = 0.8760, q2 = 0.8665, s = 8.94 for the training set and r2 = 0.9812, q2 = 0.9753, s = 7.31 for the test set. For the validation set, the statistical parameters were r2 = 0.727 and s = 12.52, but after removing the three worst outliers, the statistical parameters improved to r2 = 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.",
keywords = "CORAL software, Monte Carlo method, Penicillins, QSAR, SMILES",
author = "Veselinović, {Jovana B.} and Toropov, {Andrey A.} and Toropova, {Alla P.} and Nikolić, {Goran M.} and Veselinović, {Aleksandar M.}",
year = "2015",
doi = "10.1002/ardp.201400259",
language = "English",
volume = "348",
pages = "62--67",
journal = "Archiv der Pharmazie",
issn = "0365-6233",
publisher = "Wiley-VCH Verlag",
number = "1",

}

TY - JOUR

T1 - Monte Carlo method-based QSAR modeling of penicillins binding to human serum proteins

AU - Veselinović, Jovana B.

AU - Toropov, Andrey A.

AU - Toropova, Alla P.

AU - Nikolić, Goran M.

AU - Veselinović, Aleksandar M.

PY - 2015

Y1 - 2015

N2 - 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: r2 = 0.8760, q2 = 0.8665, s = 8.94 for the training set and r2 = 0.9812, q2 = 0.9753, s = 7.31 for the test set. For the validation set, the statistical parameters were r2 = 0.727 and s = 12.52, but after removing the three worst outliers, the statistical parameters improved to r2 = 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.

AB - 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: r2 = 0.8760, q2 = 0.8665, s = 8.94 for the training set and r2 = 0.9812, q2 = 0.9753, s = 7.31 for the test set. For the validation set, the statistical parameters were r2 = 0.727 and s = 12.52, but after removing the three worst outliers, the statistical parameters improved to r2 = 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.

KW - CORAL software

KW - Monte Carlo method

KW - Penicillins

KW - QSAR

KW - SMILES

UR - http://www.scopus.com/inward/record.url?scp=84920732465&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84920732465&partnerID=8YFLogxK

U2 - 10.1002/ardp.201400259

DO - 10.1002/ardp.201400259

M3 - Article

C2 - 25408278

AN - SCOPUS:84920732465

VL - 348

SP - 62

EP - 67

JO - Archiv der Pharmazie

JF - Archiv der Pharmazie

SN - 0365-6233

IS - 1

ER -