Racz, Anita

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orcid::0000-0001-8271-9841
  • Racz, Anita (5)
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Author's Bibliography

Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y

Racz, Anita; Andrić, Filip; Bajusz, David; Héberger, Karoly

(Springer, New York, 2018)

TY  - DATA
AU  - Racz, Anita
AU  - Andrić, Filip
AU  - Bajusz, David
AU  - Héberger, Karoly
PY  - 2018
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/3041
PB  - Springer, New York
T2  - Metabolomics
T1  - Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y
UR  - https://hdl.handle.net/21.15107/rcub_cherry_3041
ER  - 
@misc{
author = "Racz, Anita and Andrić, Filip and Bajusz, David and Héberger, Karoly",
year = "2018",
publisher = "Springer, New York",
journal = "Metabolomics",
title = "Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y",
url = "https://hdl.handle.net/21.15107/rcub_cherry_3041"
}
Racz, A., Andrić, F., Bajusz, D.,& Héberger, K.. (2018). Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y. in Metabolomics
Springer, New York..
https://hdl.handle.net/21.15107/rcub_cherry_3041
Racz A, Andrić F, Bajusz D, Héberger K. Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y. in Metabolomics. 2018;.
https://hdl.handle.net/21.15107/rcub_cherry_3041 .
Racz, Anita, Andrić, Filip, Bajusz, David, Héberger, Karoly, "Supplementary data for the article: Rácz, A.; Andrić, F.; Bajusz, D.; Héberger, K. Binary Similarity Measures for Fingerprint Analysis of Qualitative Metabolomic Profiles. Metabolomics 2018, 14 (3). https://doi.org/10.1007/s11306-018-1327-y" in Metabolomics (2018),
https://hdl.handle.net/21.15107/rcub_cherry_3041 .

Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles

Racz, Anita; Andrić, Filip; Bajusz, David; Héberger, Karoly

(Springer, New York, 2018)

TY  - JOUR
AU  - Racz, Anita
AU  - Andrić, Filip
AU  - Bajusz, David
AU  - Héberger, Karoly
PY  - 2018
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/2101
AB  - Introduction Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis. Objectives The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles. Methods Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates. Results Baroni-Urbani-Buser (BUB) and Hawkins-Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis. Conclusion Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data.
PB  - Springer, New York
T2  - Metabolomics
T1  - Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles
VL  - 14
IS  - 3
DO  - 10.1007/s11306-018-1327-y
ER  - 
@article{
author = "Racz, Anita and Andrić, Filip and Bajusz, David and Héberger, Karoly",
year = "2018",
abstract = "Introduction Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis. Objectives The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles. Methods Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates. Results Baroni-Urbani-Buser (BUB) and Hawkins-Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis. Conclusion Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data.",
publisher = "Springer, New York",
journal = "Metabolomics",
title = "Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles",
volume = "14",
number = "3",
doi = "10.1007/s11306-018-1327-y"
}
Racz, A., Andrić, F., Bajusz, D.,& Héberger, K.. (2018). Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles. in Metabolomics
Springer, New York., 14(3).
https://doi.org/10.1007/s11306-018-1327-y
Racz A, Andrić F, Bajusz D, Héberger K. Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles. in Metabolomics. 2018;14(3).
doi:10.1007/s11306-018-1327-y .
Racz, Anita, Andrić, Filip, Bajusz, David, Héberger, Karoly, "Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles" in Metabolomics, 14, no. 3 (2018),
https://doi.org/10.1007/s11306-018-1327-y . .
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Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices

Andrić, Filip; Bajusz, David; Racz, Anita; Šegan, Sandra B.; Héberger, Karoly

(Elsevier Science Bv, Amsterdam, 2016)

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/123456789/2270
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 . .
52
45
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Multivariate assessment of lipophilicity scales-computational and reversed phase thin-layer chromatographic indices

Andrić, Filip; Bajusz, David; Racz, Anita; Šegan, Sandra B.; Héberger, Karoly

(Elsevier Science Bv, Amsterdam, 2016)

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 . .
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45
52
52

Supplementary data for the article: Andrić, F.; Bajusz, D.; Rácz, A.; Šegan, S.; Héberger, K. Multivariate Assessment of Lipophilicity Scales—Computational and Reversed Phase Thin-Layer Chromatographic Indices. J. Pharm. Biomed. Anal. 2016, 127, 81–93. https://doi.org/10.1016/j.jpba.2016.04.001

Andrić, Filip; Bajusz, David; Racz, Anita; Šegan, Sandra B.; Héberger, Karoly

(Elsevier Science Bv, Amsterdam, 2016)

TY  - DATA
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/123456789/3577
PB  - Elsevier Science Bv, Amsterdam
T2  - Journal of Pharmaceutical and Biomedical Analysis
T1  - Supplementary data for the article: Andrić, F.; Bajusz, D.; Rácz, A.; Šegan, S.; Héberger, K. Multivariate Assessment of Lipophilicity Scales—Computational and Reversed Phase Thin-Layer Chromatographic Indices. J. Pharm. Biomed. Anal. 2016, 127, 81–93. https://doi.org/10.1016/j.jpba.2016.04.001
UR  - https://hdl.handle.net/21.15107/rcub_cherry_3577
ER  - 
@misc{
author = "Andrić, Filip and Bajusz, David and Racz, Anita and Šegan, Sandra B. and Héberger, Karoly",
year = "2016",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Journal of Pharmaceutical and Biomedical Analysis",
title = "Supplementary data for the article: Andrić, F.; Bajusz, D.; Rácz, A.; Šegan, S.; Héberger, K. Multivariate Assessment of Lipophilicity Scales—Computational and Reversed Phase Thin-Layer Chromatographic Indices. J. Pharm. Biomed. Anal. 2016, 127, 81–93. https://doi.org/10.1016/j.jpba.2016.04.001",
url = "https://hdl.handle.net/21.15107/rcub_cherry_3577"
}
Andrić, F., Bajusz, D., Racz, A., Šegan, S. B.,& Héberger, K.. (2016). Supplementary data for the article: Andrić, F.; Bajusz, D.; Rácz, A.; Šegan, S.; Héberger, K. Multivariate Assessment of Lipophilicity Scales—Computational and Reversed Phase Thin-Layer Chromatographic Indices. J. Pharm. Biomed. Anal. 2016, 127, 81–93. https://doi.org/10.1016/j.jpba.2016.04.001. in Journal of Pharmaceutical and Biomedical Analysis
Elsevier Science Bv, Amsterdam..
https://hdl.handle.net/21.15107/rcub_cherry_3577
Andrić F, Bajusz D, Racz A, Šegan SB, Héberger K. Supplementary data for the article: Andrić, F.; Bajusz, D.; Rácz, A.; Šegan, S.; Héberger, K. Multivariate Assessment of Lipophilicity Scales—Computational and Reversed Phase Thin-Layer Chromatographic Indices. J. Pharm. Biomed. Anal. 2016, 127, 81–93. https://doi.org/10.1016/j.jpba.2016.04.001. in Journal of Pharmaceutical and Biomedical Analysis. 2016;.
https://hdl.handle.net/21.15107/rcub_cherry_3577 .
Andrić, Filip, Bajusz, David, Racz, Anita, Šegan, Sandra B., Héberger, Karoly, "Supplementary data for the article: Andrić, F.; Bajusz, D.; Rácz, A.; Šegan, S.; Héberger, K. Multivariate Assessment of Lipophilicity Scales—Computational and Reversed Phase Thin-Layer Chromatographic Indices. J. Pharm. Biomed. Anal. 2016, 127, 81–93. https://doi.org/10.1016/j.jpba.2016.04.001" in Journal of Pharmaceutical and Biomedical Analysis (2016),
https://hdl.handle.net/21.15107/rcub_cherry_3577 .