TY  - JOUR
AU  - Pawellek, Ruben
AU  - Krmar, Jovana
AU  - Leistner, Adrian
AU  - Đajić, Nevena
AU  - Otašević, Biljana
AU  - Protić, Ana
AU  - Holzgrabe, Ulrike
PY  - 2021
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3926
AB  - The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response  independent of the analytes’ chemical structure. This study was aimed at accurately predicting the CAD response  of homologous fatty acids under varying experimental conditions. Fatty acids from C12 to C18 were used as model  substances due to semivolatile characterics that caused non-uniform CAD behaviour. Considering both experimental  conditions and molecular descriptors, a mixed quantitative structure–property relationship (QSPR) modeling was  performed using Gradient Boosted Trees (GBT ). The ensemble of 10 decisions trees (learning rate set at 0.55, the  maximal depth set at 5, and the sample rate set at 1.0) was able to explain approximately 99%  (Q2: 0.987, RMSE: 0.051)  of the observed variance in CAD responses. Validation using an external test compound confirmed the high predic- tive ability of the model established  (R2: 0.990, RMSEP: 0.050). With respect to the intrinsic attribute selection strategy,  GBT used almost all independent variables during model building. Finally, it attributed the highest importance to  the power function value, the flow rate of the mobile phase, evaporation temperature, the content of the organic  solvent in the mobile phase and the molecular descriptors such as molecular weight (MW ), Radial Distribution Func- tion—080/weighted by mass (RDF080m) and average coefficient of the last eigenvector from distance/detour matrix  (Ve2_D/Dt). The identification of the factors most relevant to the CAD responsiveness has contributed to a better  understanding of the underlying mechanisms of signal generation. An increased CAD response that was obtained for  acetone as organic modifier demonstrated its potential to replace the more expensive and environmentally harmful  acetonitrile.
PB  - BioMed Central Ltd
T2  - Journal of Cheminformatics
T1  - Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach
VL  - 13
IS  - 1
DO  - 10.1186/s13321-021-00532-0
ER  - 

@article{
author = "Pawellek, Ruben and Krmar, Jovana and Leistner, Adrian and Đajić, Nevena and Otašević, Biljana and Protić, Ana and Holzgrabe, Ulrike",
year = "2021",
abstract = "The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response  independent of the analytes’ chemical structure. This study was aimed at accurately predicting the CAD response  of homologous fatty acids under varying experimental conditions. Fatty acids from C12 to C18 were used as model  substances due to semivolatile characterics that caused non-uniform CAD behaviour. Considering both experimental  conditions and molecular descriptors, a mixed quantitative structure–property relationship (QSPR) modeling was  performed using Gradient Boosted Trees (GBT ). The ensemble of 10 decisions trees (learning rate set at 0.55, the  maximal depth set at 5, and the sample rate set at 1.0) was able to explain approximately 99%  (Q2: 0.987, RMSE: 0.051)  of the observed variance in CAD responses. Validation using an external test compound confirmed the high predic- tive ability of the model established  (R2: 0.990, RMSEP: 0.050). With respect to the intrinsic attribute selection strategy,  GBT used almost all independent variables during model building. Finally, it attributed the highest importance to  the power function value, the flow rate of the mobile phase, evaporation temperature, the content of the organic  solvent in the mobile phase and the molecular descriptors such as molecular weight (MW ), Radial Distribution Func- tion—080/weighted by mass (RDF080m) and average coefficient of the last eigenvector from distance/detour matrix  (Ve2_D/Dt). The identification of the factors most relevant to the CAD responsiveness has contributed to a better  understanding of the underlying mechanisms of signal generation. An increased CAD response that was obtained for  acetone as organic modifier demonstrated its potential to replace the more expensive and environmentally harmful  acetonitrile.",
publisher = "BioMed Central Ltd",
journal = "Journal of Cheminformatics",
title = "Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach",
volume = "13",
number = "1",
doi = "10.1186/s13321-021-00532-0"
}

Pawellek, R., Krmar, J., Leistner, A., Đajić, N., Otašević, B., Protić, A.,& Holzgrabe, U.. (2021). Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach. in Journal of Cheminformatics
BioMed Central Ltd., 13(1).
https://doi.org/10.1186/s13321-021-00532-0

Pawellek R, Krmar J, Leistner A, Đajić N, Otašević B, Protić A, Holzgrabe U. Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach. in Journal of Cheminformatics. 2021;13(1).
doi:10.1186/s13321-021-00532-0 .

Pawellek, Ruben, Krmar, Jovana, Leistner, Adrian, Đajić, Nevena, Otašević, Biljana, Protić, Ana, Holzgrabe, Ulrike, "Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach" in Journal of Cheminformatics, 13, no. 1 (2021),
https://doi.org/10.1186/s13321-021-00532-0 . .

TY  - JOUR
AU  - Schilling, Klaus
AU  - Krmar, Jovana
AU  - Maljurić, Nevena
AU  - Pawellek, Ruben
AU  - Protić, Ana
AU  - Holzgrabe, Ulrike
PY  - 2019
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3337
AB  - In this study, a quantitative structure-property relationship model was built in order to link molecular descriptors and chromatographic parameters as inputs towards CAD responsiveness. Aminoglycoside antibiotics, sugars, and acetylated amino sugars, which all lack a UV/vis chromophore, were selected as model substances due to their polar nature that represents a challenge in generating a CAD response. Acetone, PFPA, flow rate, data rate, filter constant, SM5_B(s), ATS7s, SpMin1_Bh(v), Mor09e, Mor22e, E1u, R7v+, and VP as the most influential inputs were correlated with the CAD response by virtue of ANN applying a backpropagation learning rule. External validation on previously unseen substances showed that the developed 13-6-3-1 ANN model could be used for CAD response prediction across the examined experimental domain reliably (R-2 0.989 and RMSE 0.036). The obtained network was used to reveal CAD response correlations. The impact of organic modifier content and flow rate was in accordance with the theory of the detector's functioning. Additionally, the significance of SpMin1_Bh(v) aided in emphasizing the often neglected surface-dependent CAD character, while the importance of Mor22e as a molecular descriptor accentuated its dependency on the number of electronegative atoms taking part in charging the formed particles. The significance of PFPA demonstrated the possibility of using evaporative chaotropic reagents in CAD response improvement when dealing with highly polar substances that act as kosmotropes. The network was also used in identifying possible interactions between the most significant inputs. A joint effect of PFPA and acetone was shown, representing a good starting point for further investigation with different and, especially, eco-friendly organic solvents and chaotropic agents in the routine application of CAD.
PB  - Springer Heidelberg, Heidelberg
T2  - Analytical and Bioanalytical Chemistry
T1  - Quantitative structure-property relationship modeling of polar analytes lacking UV chromophores to charged aerosol detector response
VL  - 411
IS  - 13
SP  - 2945
EP  - 2959
DO  - 10.1007/s00216-019-01744-y
ER  - 

@article{
author = "Schilling, Klaus and Krmar, Jovana and Maljurić, Nevena and Pawellek, Ruben and Protić, Ana and Holzgrabe, Ulrike",
year = "2019",
abstract = "In this study, a quantitative structure-property relationship model was built in order to link molecular descriptors and chromatographic parameters as inputs towards CAD responsiveness. Aminoglycoside antibiotics, sugars, and acetylated amino sugars, which all lack a UV/vis chromophore, were selected as model substances due to their polar nature that represents a challenge in generating a CAD response. Acetone, PFPA, flow rate, data rate, filter constant, SM5_B(s), ATS7s, SpMin1_Bh(v), Mor09e, Mor22e, E1u, R7v+, and VP as the most influential inputs were correlated with the CAD response by virtue of ANN applying a backpropagation learning rule. External validation on previously unseen substances showed that the developed 13-6-3-1 ANN model could be used for CAD response prediction across the examined experimental domain reliably (R-2 0.989 and RMSE 0.036). The obtained network was used to reveal CAD response correlations. The impact of organic modifier content and flow rate was in accordance with the theory of the detector's functioning. Additionally, the significance of SpMin1_Bh(v) aided in emphasizing the often neglected surface-dependent CAD character, while the importance of Mor22e as a molecular descriptor accentuated its dependency on the number of electronegative atoms taking part in charging the formed particles. The significance of PFPA demonstrated the possibility of using evaporative chaotropic reagents in CAD response improvement when dealing with highly polar substances that act as kosmotropes. The network was also used in identifying possible interactions between the most significant inputs. A joint effect of PFPA and acetone was shown, representing a good starting point for further investigation with different and, especially, eco-friendly organic solvents and chaotropic agents in the routine application of CAD.",
publisher = "Springer Heidelberg, Heidelberg",
journal = "Analytical and Bioanalytical Chemistry",
title = "Quantitative structure-property relationship modeling of polar analytes lacking UV chromophores to charged aerosol detector response",
volume = "411",
number = "13",
pages = "2945-2959",
doi = "10.1007/s00216-019-01744-y"
}

Schilling, K., Krmar, J., Maljurić, N., Pawellek, R., Protić, A.,& Holzgrabe, U.. (2019). Quantitative structure-property relationship modeling of polar analytes lacking UV chromophores to charged aerosol detector response. in Analytical and Bioanalytical Chemistry
Springer Heidelberg, Heidelberg., 411(13), 2945-2959.
https://doi.org/10.1007/s00216-019-01744-y

Schilling K, Krmar J, Maljurić N, Pawellek R, Protić A, Holzgrabe U. Quantitative structure-property relationship modeling of polar analytes lacking UV chromophores to charged aerosol detector response. in Analytical and Bioanalytical Chemistry. 2019;411(13):2945-2959.
doi:10.1007/s00216-019-01744-y .

Schilling, Klaus, Krmar, Jovana, Maljurić, Nevena, Pawellek, Ruben, Protić, Ana, Holzgrabe, Ulrike, "Quantitative structure-property relationship modeling of polar analytes lacking UV chromophores to charged aerosol detector response" in Analytical and Bioanalytical Chemistry, 411, no. 13 (2019):2945-2959,
https://doi.org/10.1007/s00216-019-01744-y . .