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Structure-retention relationship study of arylpiperazines by linear multivariate modeling

Authorized Users Only
2010
Authors
Trifković, Jelena
Andrić, Filip
Ristivojević, Petar
Andrić, Deana
Tešić, Živoslav Lj.
Milojković-Opsenica, Dušanka
Article (Published version)
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Abstract
A quantitative structure retention relationship study has been performed to correlate the retention of 33 newly synthesized arylpiperazines with their molecular characteristics, using thin-layer chromatography. Principal component analysis followed by multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) was performed to identify the most important factors, to quantify their influences, and to select descriptors that best describe the behavior of the compounds investigated. The best statistical performance was achieved by applying PLS regression, leading to the lowest value of the standard error (root mean square errors of calibration of 0.159 and cross-validated value RMSE cross-validation = 0.231 units), followed by the PCR (root mean square errors of calibration = 0.195 and RMSE cross-validation = 0.305) and MLR (R(adj)(2) = 0 9499, F = 102.017, mean square error = 0.052 and predicted residual error sum of squares = 2.23). Two factors... of the highest influence: surface tension and hydrophilic lipophilic balance appear as the part of obtained models. In addition, polar surface area and hydrophilic surface area are included by both PLS and PCR models. Moreover, log P has been added to the PLS model. Besides, PCR model includes following descriptors: hydrogen bond acceptor, hydrogen bond donor and LUMO energy, whereas topological descriptors: connectivity indices 0 and 2, and valence index 3 are included in the MLR model.

Keywords:
Multiple linear regression / Partial least squares / Principal component analysis / Principal component regression / Quantitative structure-retention relationship
Source:
Journal of Separation Science, 2010, 33, 17-18, 2619-2628
Publisher:
  • Wiley-V C H Verlag Gmbh, Weinheim
Funding / projects:
  • Sinteza, analiza i aktivnost novih organskih polidentatnih liganada i njihovih kompleksa sa d-metalima (RS-142062)

DOI: 10.1002/jssc.201000200

ISSN: 1615-9306

PubMed: 20665766

WoS: 000282612900009

Scopus: 2-s2.0-77956942643
[ Google Scholar ]
24
22
URI
https://cherry.chem.bg.ac.rs/handle/123456789/1124
Collections
  • Publikacije
  • Publikacije
Institution/Community
Hemijski fakultet
TY  - JOUR
AU  - Trifković, Jelena
AU  - Andrić, Filip
AU  - Ristivojević, Petar
AU  - Andrić, Deana
AU  - Tešić, Živoslav Lj.
AU  - Milojković-Opsenica, Dušanka
PY  - 2010
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/1124
AB  - A quantitative structure retention relationship study has been performed to correlate the retention of 33 newly synthesized arylpiperazines with their molecular characteristics, using thin-layer chromatography. Principal component analysis followed by multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) was performed to identify the most important factors, to quantify their influences, and to select descriptors that best describe the behavior of the compounds investigated. The best statistical performance was achieved by applying PLS regression, leading to the lowest value of the standard error (root mean square errors of calibration of 0.159 and cross-validated value RMSE cross-validation = 0.231 units), followed by the PCR (root mean square errors of calibration = 0.195 and RMSE cross-validation = 0.305) and MLR (R(adj)(2) = 0 9499, F = 102.017, mean square error = 0.052 and predicted residual error sum of squares = 2.23). Two factors of the highest influence: surface tension and hydrophilic lipophilic balance appear as the part of obtained models. In addition, polar surface area and hydrophilic surface area are included by both PLS and PCR models. Moreover, log P has been added to the PLS model. Besides, PCR model includes following descriptors: hydrogen bond acceptor, hydrogen bond donor and LUMO energy, whereas topological descriptors: connectivity indices 0 and 2, and valence index 3 are included in the MLR model.
PB  - Wiley-V C H Verlag Gmbh, Weinheim
T2  - Journal of Separation Science
T1  - Structure-retention relationship study of arylpiperazines by linear multivariate modeling
VL  - 33
IS  - 17-18
SP  - 2619
EP  - 2628
DO  - 10.1002/jssc.201000200
UR  - Kon_2124
ER  - 
@article{
author = "Trifković, Jelena and Andrić, Filip and Ristivojević, Petar and Andrić, Deana and Tešić, Živoslav Lj. and Milojković-Opsenica, Dušanka",
year = "2010",
abstract = "A quantitative structure retention relationship study has been performed to correlate the retention of 33 newly synthesized arylpiperazines with their molecular characteristics, using thin-layer chromatography. Principal component analysis followed by multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) was performed to identify the most important factors, to quantify their influences, and to select descriptors that best describe the behavior of the compounds investigated. The best statistical performance was achieved by applying PLS regression, leading to the lowest value of the standard error (root mean square errors of calibration of 0.159 and cross-validated value RMSE cross-validation = 0.231 units), followed by the PCR (root mean square errors of calibration = 0.195 and RMSE cross-validation = 0.305) and MLR (R(adj)(2) = 0 9499, F = 102.017, mean square error = 0.052 and predicted residual error sum of squares = 2.23). Two factors of the highest influence: surface tension and hydrophilic lipophilic balance appear as the part of obtained models. In addition, polar surface area and hydrophilic surface area are included by both PLS and PCR models. Moreover, log P has been added to the PLS model. Besides, PCR model includes following descriptors: hydrogen bond acceptor, hydrogen bond donor and LUMO energy, whereas topological descriptors: connectivity indices 0 and 2, and valence index 3 are included in the MLR model.",
publisher = "Wiley-V C H Verlag Gmbh, Weinheim",
journal = "Journal of Separation Science",
title = "Structure-retention relationship study of arylpiperazines by linear multivariate modeling",
volume = "33",
number = "17-18",
pages = "2619-2628",
doi = "10.1002/jssc.201000200",
url = "Kon_2124"
}
Trifković, J., Andrić, F., Ristivojević, P., Andrić, D., Tešić, Ž. Lj.,& Milojković-Opsenica, D.. (2010). Structure-retention relationship study of arylpiperazines by linear multivariate modeling. in Journal of Separation Science
Wiley-V C H Verlag Gmbh, Weinheim., 33(17-18), 2619-2628.
https://doi.org/10.1002/jssc.201000200
Kon_2124
Trifković J, Andrić F, Ristivojević P, Andrić D, Tešić ŽL, Milojković-Opsenica D. Structure-retention relationship study of arylpiperazines by linear multivariate modeling. in Journal of Separation Science. 2010;33(17-18):2619-2628.
doi:10.1002/jssc.201000200
Kon_2124 .
Trifković, Jelena, Andrić, Filip, Ristivojević, Petar, Andrić, Deana, Tešić, Živoslav Lj., Milojković-Opsenica, Dušanka, "Structure-retention relationship study of arylpiperazines by linear multivariate modeling" in Journal of Separation Science, 33, no. 17-18 (2010):2619-2628,
https://doi.org/10.1002/jssc.201000200 .,
Kon_2124 .

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