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 . .