TY  - JOUR
AU  - Héberger, Karoly
AU  - Sakan, Sanja M.
AU  - Škrbić, Biljana
AU  - Popović, Aleksandar R.
AU  - Đorđević, Dragana S.
AU  - Relić, Dubravka
PY  - 2018
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/3250
AB  - This study aims to compare three extraction techniques of four sequential element extraction steps from soil and sediment samples that were taken from the location of the Pancevo petrochemical industry (Serbia). Elements were extracted using three different techniques: conventional, microwave and ultrasound extraction. A novel procedure sum of the ranking differences (SRD) - was able to rank the techniques and elements, to see whether this method is a suitable tool to reveal the similarities and dissimilarities in element extraction techniques, provided that a proper ranking reference is available. The concentrations of the following elements Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Si, Sn, Sr, V and Zn were determined through ICP OES. The different efficiencies and recovery values of element concentrations using each of the three extraction techniques were examined by the CRM BCR-701. By using SRD, we obtained a better separation between the different extraction techniques and steps when we rank their differences among the samples while lower separation was obtained according to analysed elements. Appling this method for ordering the elements could be useful for three purposes: (i) to find possible associations among the elements; (ii) to find possible elements that have outlier concentrations or (iii) detect differences in geochemical origin or behaviour of elements. Cross-validation of the SRD values in combination with cluster and principal component analysis revealed the same groups of extraction steps and techniques. (C) 2018 Elsevier Ltd. All rights reserved.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Chemosphere
T1  - Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods
VL  - 198
SP  - 103
EP  - 110
DO  - 10.1016/j.chemosphere.2017.12.200
ER  - 

@article{
author = "Héberger, Karoly and Sakan, Sanja M. and Škrbić, Biljana and Popović, Aleksandar R. and Đorđević, Dragana S. and Relić, Dubravka",
year = "2018",
abstract = "This study aims to compare three extraction techniques of four sequential element extraction steps from soil and sediment samples that were taken from the location of the Pancevo petrochemical industry (Serbia). Elements were extracted using three different techniques: conventional, microwave and ultrasound extraction. A novel procedure sum of the ranking differences (SRD) - was able to rank the techniques and elements, to see whether this method is a suitable tool to reveal the similarities and dissimilarities in element extraction techniques, provided that a proper ranking reference is available. The concentrations of the following elements Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Si, Sn, Sr, V and Zn were determined through ICP OES. The different efficiencies and recovery values of element concentrations using each of the three extraction techniques were examined by the CRM BCR-701. By using SRD, we obtained a better separation between the different extraction techniques and steps when we rank their differences among the samples while lower separation was obtained according to analysed elements. Appling this method for ordering the elements could be useful for three purposes: (i) to find possible associations among the elements; (ii) to find possible elements that have outlier concentrations or (iii) detect differences in geochemical origin or behaviour of elements. Cross-validation of the SRD values in combination with cluster and principal component analysis revealed the same groups of extraction steps and techniques. (C) 2018 Elsevier Ltd. All rights reserved.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Chemosphere",
title = "Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods",
volume = "198",
pages = "103-110",
doi = "10.1016/j.chemosphere.2017.12.200"
}

Héberger, K., Sakan, S. M., Škrbić, B., Popović, A. R., Đorđević, D. S.,& Relić, D.. (2018). Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods. in Chemosphere
Pergamon-Elsevier Science Ltd, Oxford., 198, 103-110.
https://doi.org/10.1016/j.chemosphere.2017.12.200

Héberger K, Sakan SM, Škrbić B, Popović AR, Đorđević DS, Relić D. Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods. in Chemosphere. 2018;198:103-110.
doi:10.1016/j.chemosphere.2017.12.200 .

Héberger, Karoly, Sakan, Sanja M., Škrbić, Biljana, Popović, Aleksandar R., Đorđević, Dragana S., Relić, Dubravka, "Ranking and similarity of conventional, microwave and ultrasound element sequential extraction methods" in Chemosphere, 198 (2018):103-110,
https://doi.org/10.1016/j.chemosphere.2017.12.200 . .

TY  - DATA
AU  - Héberger, Karoly
AU  - Sakan, Sanja M.
AU  - Škrbić, Biljana
AU  - Popović, Aleksandar R.
AU  - Đorđević, Dragana S.
AU  - Relić, Dubravka
PY  - 2018
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/3251
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Chemosphere
T1  - Supplementary material for the article: Relić, D.; Héberger, K.; Sakan, S.; Škrbić, B.; Popović, A.; Đorđević, D. Ranking and Similarity of Conventional, Microwave and Ultrasound Element Sequential Extraction Methods. Chemosphere 2018, 198, 103–110. https://doi.org/10.1016/j.chemosphere.2017.12.200
UR  - https://hdl.handle.net/21.15107/rcub_cherry_3251
ER  - 

@misc{
author = "Héberger, Karoly and Sakan, Sanja M. and Škrbić, Biljana and Popović, Aleksandar R. and Đorđević, Dragana S. and Relić, Dubravka",
year = "2018",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Chemosphere",
title = "Supplementary material for the article: Relić, D.; Héberger, K.; Sakan, S.; Škrbić, B.; Popović, A.; Đorđević, D. Ranking and Similarity of Conventional, Microwave and Ultrasound Element Sequential Extraction Methods. Chemosphere 2018, 198, 103–110. https://doi.org/10.1016/j.chemosphere.2017.12.200",
url = "https://hdl.handle.net/21.15107/rcub_cherry_3251"
}

Héberger, K., Sakan, S. M., Škrbić, B., Popović, A. R., Đorđević, D. S.,& Relić, D.. (2018). Supplementary material for the article: Relić, D.; Héberger, K.; Sakan, S.; Škrbić, B.; Popović, A.; Đorđević, D. Ranking and Similarity of Conventional, Microwave and Ultrasound Element Sequential Extraction Methods. Chemosphere 2018, 198, 103–110. https://doi.org/10.1016/j.chemosphere.2017.12.200. in Chemosphere
Pergamon-Elsevier Science Ltd, Oxford..
https://hdl.handle.net/21.15107/rcub_cherry_3251

Héberger K, Sakan SM, Škrbić B, Popović AR, Đorđević DS, Relić D. Supplementary material for the article: Relić, D.; Héberger, K.; Sakan, S.; Škrbić, B.; Popović, A.; Đorđević, D. Ranking and Similarity of Conventional, Microwave and Ultrasound Element Sequential Extraction Methods. Chemosphere 2018, 198, 103–110. https://doi.org/10.1016/j.chemosphere.2017.12.200. in Chemosphere. 2018;.
https://hdl.handle.net/21.15107/rcub_cherry_3251 .

Héberger, Karoly, Sakan, Sanja M., Škrbić, Biljana, Popović, Aleksandar R., Đorđević, Dragana S., Relić, Dubravka, "Supplementary material for the article: Relić, D.; Héberger, K.; Sakan, S.; Škrbić, B.; Popović, A.; Đorđević, D. Ranking and Similarity of Conventional, Microwave and Ultrasound Element Sequential Extraction Methods. Chemosphere 2018, 198, 103–110. https://doi.org/10.1016/j.chemosphere.2017.12.200" in Chemosphere (2018),
https://hdl.handle.net/21.15107/rcub_cherry_3251 .

TY  - JOUR
AU  - Andrić, Filip
AU  - Héberger, Karoly
PY  - 2017
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/3219
AB  - Comparison and selection of chromatographic columns is an important part of development as well as validation of analytical methods. Presently there is abundant number of methods for selection of the most similar and orthogonal columns, based on the application of limited number of test compounds as well as quantitative structure retention relationship models (QSRR), from among Snyder's hydrophobic subtraction model (HSM) have been most extensively used. Chromatographic data of 67 compounds were evaluated using principal component analysis (PCA), hierarchical cluster analysis (HCA), non-parametric ranking methods as sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM), both applied as a consensus driven comparison, and complemented by the comparison with one variable at a time (COVAT) approach. The aim was to compare the ability of the HSM approach and the approach based on primary retention data of test solutes (logic values) to differentiate among ten highly similar C18 columns. The ranking (clustering) pattern of chromatographic columns based on primary retention data and HSM parameters gave different results in all instances. Patterns based on retention coefficients were in accordance with expectations based on columns' physicochemical parameters, while HSM parameters provided a different clustering. Similarity indices calculated from the following dissimilarity measures: SRD, GPCM Fisher's conditional exact probability weighted (CEPW) scores; Euclidian, Manhattan, Chebyshev, and cosine distances; Pear son's, Spearman's, and Kendall's, correlation coefficients have been ranked by the consensus based SRD. Analysis of variance confirmed that the HSM model produced statistically significant increases of SRD values for the majority of similarity indices, i.e. HS transformation of original retention data yields significant loss of information, and finally results in lower performance of HSM methodology. The best similarity measures were obtained using primary retention data, and derived from Kendal's and Spearman's correlation coefficients, as well as GPCM and SRD score values. Selectivity function, Fs, originally proposed by Snyder, demonstrated moderate performance.
PB  - Elsevier Science Bv, Amsterdam
T2  - Journal of Chromatography A
T1  - How to compare separation selectivity of high-performance liquid chromatographic columns properly?
VL  - 1488
SP  - 45
EP  - 56
DO  - 10.1016/j.chroma.2017.01.066
ER  - 

@article{
author = "Andrić, Filip and Héberger, Karoly",
year = "2017",
abstract = "Comparison and selection of chromatographic columns is an important part of development as well as validation of analytical methods. Presently there is abundant number of methods for selection of the most similar and orthogonal columns, based on the application of limited number of test compounds as well as quantitative structure retention relationship models (QSRR), from among Snyder's hydrophobic subtraction model (HSM) have been most extensively used. Chromatographic data of 67 compounds were evaluated using principal component analysis (PCA), hierarchical cluster analysis (HCA), non-parametric ranking methods as sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM), both applied as a consensus driven comparison, and complemented by the comparison with one variable at a time (COVAT) approach. The aim was to compare the ability of the HSM approach and the approach based on primary retention data of test solutes (logic values) to differentiate among ten highly similar C18 columns. The ranking (clustering) pattern of chromatographic columns based on primary retention data and HSM parameters gave different results in all instances. Patterns based on retention coefficients were in accordance with expectations based on columns' physicochemical parameters, while HSM parameters provided a different clustering. Similarity indices calculated from the following dissimilarity measures: SRD, GPCM Fisher's conditional exact probability weighted (CEPW) scores; Euclidian, Manhattan, Chebyshev, and cosine distances; Pear son's, Spearman's, and Kendall's, correlation coefficients have been ranked by the consensus based SRD. Analysis of variance confirmed that the HSM model produced statistically significant increases of SRD values for the majority of similarity indices, i.e. HS transformation of original retention data yields significant loss of information, and finally results in lower performance of HSM methodology. The best similarity measures were obtained using primary retention data, and derived from Kendal's and Spearman's correlation coefficients, as well as GPCM and SRD score values. Selectivity function, Fs, originally proposed by Snyder, demonstrated moderate performance.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Journal of Chromatography A",
title = "How to compare separation selectivity of high-performance liquid chromatographic columns properly?",
volume = "1488",
pages = "45-56",
doi = "10.1016/j.chroma.2017.01.066"
}

Andrić, F.,& Héberger, K.. (2017). How to compare separation selectivity of high-performance liquid chromatographic columns properly?. in Journal of Chromatography A
Elsevier Science Bv, Amsterdam., 1488, 45-56.
https://doi.org/10.1016/j.chroma.2017.01.066

Andrić F, Héberger K. How to compare separation selectivity of high-performance liquid chromatographic columns properly?. in Journal of Chromatography A. 2017;1488:45-56.
doi:10.1016/j.chroma.2017.01.066 .

Andrić, Filip, Héberger, Karoly, "How to compare separation selectivity of high-performance liquid chromatographic columns properly?" in Journal of Chromatography A, 1488 (2017):45-56,
https://doi.org/10.1016/j.chroma.2017.01.066 . .

TY  - DATA
AU  - Andrić, Filip
AU  - Héberger, Karoly
PY  - 2017
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/3220
PB  - Elsevier Science Bv, Amsterdam
T2  - Journal of Chromatography A
T1  - Supplementary material for the article: Andrić, F.; Héberger, K. How to Compare Separation Selectivity of High-Performance Liquid Chromatographic Columns Properly? Journal of Chromatography A 2017, 1488, 45–56. https://doi.org/10.1016/j.chroma.2017.01.066
UR  - https://hdl.handle.net/21.15107/rcub_cherry_3220
ER  - 

@misc{
author = "Andrić, Filip and Héberger, Karoly",
year = "2017",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Journal of Chromatography A",
title = "Supplementary material for the article: Andrić, F.; Héberger, K. How to Compare Separation Selectivity of High-Performance Liquid Chromatographic Columns Properly? Journal of Chromatography A 2017, 1488, 45–56. https://doi.org/10.1016/j.chroma.2017.01.066",
url = "https://hdl.handle.net/21.15107/rcub_cherry_3220"
}

Andrić, F.,& Héberger, K.. (2017). Supplementary material for the article: Andrić, F.; Héberger, K. How to Compare Separation Selectivity of High-Performance Liquid Chromatographic Columns Properly? Journal of Chromatography A 2017, 1488, 45–56. https://doi.org/10.1016/j.chroma.2017.01.066. in Journal of Chromatography A
Elsevier Science Bv, Amsterdam..
https://hdl.handle.net/21.15107/rcub_cherry_3220

Andrić F, Héberger K. Supplementary material for the article: Andrić, F.; Héberger, K. How to Compare Separation Selectivity of High-Performance Liquid Chromatographic Columns Properly? Journal of Chromatography A 2017, 1488, 45–56. https://doi.org/10.1016/j.chroma.2017.01.066. in Journal of Chromatography A. 2017;.
https://hdl.handle.net/21.15107/rcub_cherry_3220 .

Andrić, Filip, Héberger, Karoly, "Supplementary material for the article: Andrić, F.; Héberger, K. How to Compare Separation Selectivity of High-Performance Liquid Chromatographic Columns Properly? Journal of Chromatography A 2017, 1488, 45–56. https://doi.org/10.1016/j.chroma.2017.01.066" in Journal of Chromatography A (2017),
https://hdl.handle.net/21.15107/rcub_cherry_3220 .

TY  - JOUR
AU  - Andrić, Filip
AU  - Bajusz, David
AU  - Racz, Anita
AU  - Šegan, Sandra B.
AU  - Héberger, Karoly
PY  - 2016
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/3576
AB  - Needs for fast, yet reliable means of assessing the lipophilicities of diverse compounds resulted in the development of various in silico and chromatographic approaches that are faster, cheaper, and greener compared to the traditional shake-flask method. However, at present no accepted "standard" approach exists for their comparison and selection of the most appropriate one(s). This is of utmost importance when it comes to the development of new lipophilicity indices, or the assessment of the lipophilicity of newly synthesized compounds. In this study, 50 well-known, diverse compounds of significant pharmaceutical and environmental importance have been selected and examined. Octanol-water partition coefficients have been measured with the shake-flask method for most of them. Their retentions have been studied in typical reversed thin-layer chromatographic systems, involving the most frequently employed stationary phases (octadecyl- and cyano-modified silica), and acetonitrile and methanol as mobile phase constituents. Twelve computationally estimated logP-s and twenty chromatographic indices together with the shake-flask octanol-water partition coefficient have been investigated with classical chemometric approaches such as principal component analysis (PCA), hierarchical cluster analysis (HCA), Pearson's and Spearman's correlation matrices, as well as novel non-parametric methods: sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM). Novel SRD and GPCM methods have been introduced based on the Comparisons with One VAriable (lipophilicity metric) at a Time (COVAT). For the visualization of COVAT results, a heatmap format was introduced. Analysis of variance (ANOVA) was applied to reveal the dominant factors between computational logPs and various chromatographic measures. In consensus-based comparisons, the shake-flask method performed the best, closely followed by computational estimates, while the chromatographic estimates often overlap with in silico assessments, mostly with methods involving octadecyl-modified silica stationary phases. The ones that employ cyano-modified silica perform generally worse. The introduction of alternative coloring schemes for the covariance matrices and SRD/GPCM heatmaps enables the discovery of intrinsic relationships among lipophilicity scales and the selection of best/worst measures. Closest to the recommended logK(ow) values are ClogP and the first principal component scores obtained on octadecyl-silica stationary phase in combination with methanol-water mobile phase, while the usage of slopes derived from Soczewinski-Matyisik equation should be avoided. (C) 2016 Elsevier B.V. All rights reserved.
PB  - Elsevier Science Bv, Amsterdam
T2  - Journal of Pharmaceutical and Biomedical Analysis
T1  - Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices
VL  - 127
SP  - 81
EP  - 93
DO  - 10.1016/j.jpba.2016.04.001
ER  - 

@article{
author = "Andrić, Filip and Bajusz, David and Racz, Anita and Šegan, Sandra B. and Héberger, Karoly",
year = "2016",
abstract = "Needs for fast, yet reliable means of assessing the lipophilicities of diverse compounds resulted in the development of various in silico and chromatographic approaches that are faster, cheaper, and greener compared to the traditional shake-flask method. However, at present no accepted "standard" approach exists for their comparison and selection of the most appropriate one(s). This is of utmost importance when it comes to the development of new lipophilicity indices, or the assessment of the lipophilicity of newly synthesized compounds. In this study, 50 well-known, diverse compounds of significant pharmaceutical and environmental importance have been selected and examined. Octanol-water partition coefficients have been measured with the shake-flask method for most of them. Their retentions have been studied in typical reversed thin-layer chromatographic systems, involving the most frequently employed stationary phases (octadecyl- and cyano-modified silica), and acetonitrile and methanol as mobile phase constituents. Twelve computationally estimated logP-s and twenty chromatographic indices together with the shake-flask octanol-water partition coefficient have been investigated with classical chemometric approaches such as principal component analysis (PCA), hierarchical cluster analysis (HCA), Pearson's and Spearman's correlation matrices, as well as novel non-parametric methods: sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM). Novel SRD and GPCM methods have been introduced based on the Comparisons with One VAriable (lipophilicity metric) at a Time (COVAT). For the visualization of COVAT results, a heatmap format was introduced. Analysis of variance (ANOVA) was applied to reveal the dominant factors between computational logPs and various chromatographic measures. In consensus-based comparisons, the shake-flask method performed the best, closely followed by computational estimates, while the chromatographic estimates often overlap with in silico assessments, mostly with methods involving octadecyl-modified silica stationary phases. The ones that employ cyano-modified silica perform generally worse. The introduction of alternative coloring schemes for the covariance matrices and SRD/GPCM heatmaps enables the discovery of intrinsic relationships among lipophilicity scales and the selection of best/worst measures. Closest to the recommended logK(ow) values are ClogP and the first principal component scores obtained on octadecyl-silica stationary phase in combination with methanol-water mobile phase, while the usage of slopes derived from Soczewinski-Matyisik equation should be avoided. (C) 2016 Elsevier B.V. All rights reserved.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Journal of Pharmaceutical and Biomedical Analysis",
title = "Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices",
volume = "127",
pages = "81-93",
doi = "10.1016/j.jpba.2016.04.001"
}

Andrić, F., Bajusz, D., Racz, A., Šegan, S. B.,& Héberger, K.. (2016). Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices. in Journal of Pharmaceutical and Biomedical Analysis
Elsevier Science Bv, Amsterdam., 127, 81-93.
https://doi.org/10.1016/j.jpba.2016.04.001

Andrić F, Bajusz D, Racz A, Šegan SB, Héberger K. Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices. in Journal of Pharmaceutical and Biomedical Analysis. 2016;127:81-93.
doi:10.1016/j.jpba.2016.04.001 .

Andrić, Filip, Bajusz, David, Racz, Anita, Šegan, Sandra B., Héberger, Karoly, "Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices" in Journal of Pharmaceutical and Biomedical Analysis, 127 (2016):81-93,
https://doi.org/10.1016/j.jpba.2016.04.001 . .

TY  - DATA
AU  - Andrić, Filip
AU  - Héberger, Karoly
PY  - 2015
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/3354
PB  - Elsevier Science Bv, Amsterdam
T2  - Journal of Chromatography A
T1  - Supplementary data for article: Andrić, F.; Héberger, K. Chromatographic and Computational Assessment of Lipophilicity Using Sum of Ranking Differences and Generalized Pair-Correlation. Journal of Chromatography A 2015, 1380, 130–138. https://doi.org/10.1016/j.chroma.2014.12.073
UR  - https://hdl.handle.net/21.15107/rcub_cherry_3354
ER  - 

@misc{
author = "Andrić, Filip and Héberger, Karoly",
year = "2015",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Journal of Chromatography A",
title = "Supplementary data for article: Andrić, F.; Héberger, K. Chromatographic and Computational Assessment of Lipophilicity Using Sum of Ranking Differences and Generalized Pair-Correlation. Journal of Chromatography A 2015, 1380, 130–138. https://doi.org/10.1016/j.chroma.2014.12.073",
url = "https://hdl.handle.net/21.15107/rcub_cherry_3354"
}

Andrić, F.,& Héberger, K.. (2015). Supplementary data for article: Andrić, F.; Héberger, K. Chromatographic and Computational Assessment of Lipophilicity Using Sum of Ranking Differences and Generalized Pair-Correlation. Journal of Chromatography A 2015, 1380, 130–138. https://doi.org/10.1016/j.chroma.2014.12.073. in Journal of Chromatography A
Elsevier Science Bv, Amsterdam..
https://hdl.handle.net/21.15107/rcub_cherry_3354

Andrić F, Héberger K. Supplementary data for article: Andrić, F.; Héberger, K. Chromatographic and Computational Assessment of Lipophilicity Using Sum of Ranking Differences and Generalized Pair-Correlation. Journal of Chromatography A 2015, 1380, 130–138. https://doi.org/10.1016/j.chroma.2014.12.073. in Journal of Chromatography A. 2015;.
https://hdl.handle.net/21.15107/rcub_cherry_3354 .

Andrić, Filip, Héberger, Karoly, "Supplementary data for article: Andrić, F.; Héberger, K. Chromatographic and Computational Assessment of Lipophilicity Using Sum of Ranking Differences and Generalized Pair-Correlation. Journal of Chromatography A 2015, 1380, 130–138. https://doi.org/10.1016/j.chroma.2014.12.073" in Journal of Chromatography A (2015),
https://hdl.handle.net/21.15107/rcub_cherry_3354 .

TY  - DATA
AU  - Andrić, Filip
AU  - Héberger, Karoly
PY  - 2015
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/3456
PB  - Elsevier Science Bv, Amsterdam
T2  - Journal of Pharmaceutical and Biomedical Analysis
T1  - Supplementary data for article: Andrić, F.; Héberger, K. Towards Better Understanding of Lipophilicity: Assessment of in Silico and Chromatographic LogP Measures for Pharmaceutically Important Compounds by Nonparametric Rankings. Journal of Pharmaceutical and Biomedical Analysis 2015, 115, 183–191. https://doi.org/10.1016/j.jpba.2015.07.006
UR  - https://hdl.handle.net/21.15107/rcub_cherry_3456
ER  - 

@misc{
author = "Andrić, Filip and Héberger, Karoly",
year = "2015",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Journal of Pharmaceutical and Biomedical Analysis",
title = "Supplementary data for article: Andrić, F.; Héberger, K. Towards Better Understanding of Lipophilicity: Assessment of in Silico and Chromatographic LogP Measures for Pharmaceutically Important Compounds by Nonparametric Rankings. Journal of Pharmaceutical and Biomedical Analysis 2015, 115, 183–191. https://doi.org/10.1016/j.jpba.2015.07.006",
url = "https://hdl.handle.net/21.15107/rcub_cherry_3456"
}

Andrić, F.,& Héberger, K.. (2015). Supplementary data for article: Andrić, F.; Héberger, K. Towards Better Understanding of Lipophilicity: Assessment of in Silico and Chromatographic LogP Measures for Pharmaceutically Important Compounds by Nonparametric Rankings. Journal of Pharmaceutical and Biomedical Analysis 2015, 115, 183–191. https://doi.org/10.1016/j.jpba.2015.07.006. in Journal of Pharmaceutical and Biomedical Analysis
Elsevier Science Bv, Amsterdam..
https://hdl.handle.net/21.15107/rcub_cherry_3456

Andrić F, Héberger K. Supplementary data for article: Andrić, F.; Héberger, K. Towards Better Understanding of Lipophilicity: Assessment of in Silico and Chromatographic LogP Measures for Pharmaceutically Important Compounds by Nonparametric Rankings. Journal of Pharmaceutical and Biomedical Analysis 2015, 115, 183–191. https://doi.org/10.1016/j.jpba.2015.07.006. in Journal of Pharmaceutical and Biomedical Analysis. 2015;.
https://hdl.handle.net/21.15107/rcub_cherry_3456 .

Andrić, Filip, Héberger, Karoly, "Supplementary data for article: Andrić, F.; Héberger, K. Towards Better Understanding of Lipophilicity: Assessment of in Silico and Chromatographic LogP Measures for Pharmaceutically Important Compounds by Nonparametric Rankings. Journal of Pharmaceutical and Biomedical Analysis 2015, 115, 183–191. https://doi.org/10.1016/j.jpba.2015.07.006" in Journal of Pharmaceutical and Biomedical Analysis (2015),
https://hdl.handle.net/21.15107/rcub_cherry_3456 .