Structure-retention relationship study of arylpiperazines by linear multivariate modeling
Samo za registrovane korisnike
2010
Autori
Trifković, JelenaAndrić, Filip
Ristivojević, Petar
Andrić, Deana
Tešić, Živoslav Lj.
Milojković-Opsenica, Dušanka
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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.
Ključne reči:
Multiple linear regression / Partial least squares / Principal component analysis / Principal component regression / Quantitative structure-retention relationshipIzvor:
Journal of Separation Science, 2010, 33, 17-18, 2619-2628Izdavač:
- Wiley-V C H Verlag Gmbh, Weinheim
Finansiranje / projekti:
- Sinteza, analiza i aktivnost novih organskih polidentatnih liganada i njihovih kompleksa sa d-metalima (RS-MESTD-MPN2006-2010-142062)
DOI: 10.1002/jssc.201000200
ISSN: 1615-9306
PubMed: 20665766
WoS: 000282612900009
Scopus: 2-s2.0-77956942643
Institucija/grupa
Hemijski fakultet / Faculty of ChemistryTY - 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 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" }
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
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 .
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 . .