Why choose between prediction power and mechanistic insight in QSRR modeling? A case study of artificial membrane chromatography and machine learning

Wiktor Nisterenko, Julia Nicman, Jiayin Deng, Yash Raj Singh, Alicja Różycka, Katarzyna Ewa Greber, Daniel Szulczyk, Michał Nowacki, Maciej Dawidowski, Anna Drabczyk, Jolanta Jaśkowska, Przemysław Zaręba, Damian Kułaga, Marek Staszewski, Monika Wujec, Piotr Krzeczyński, Olga Michalak, Greta Klejborowska, Camilla Scarpellini, Koen Augustyns, Jaka Dernovšek, Tihomir Tomašič, Jan Korabecny, Joanna Fedorowicz, Jarosław Sączewski, Wiesław Sawicki, Defang Ouyang, Krzesimir Ciura

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DOI: 10.1016/j.aca.2026.346201
Published: 2026-11
Container: Analytica Chimica Acta
Publisher: Elsevier BV
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1016/j.aca.2026.346201,
  title = {Why choose between prediction power and mechanistic insight in QSRR modeling? A case study of artificial membrane chromatography and machine learning},
  author = {Wiktor Nisterenko and Julia Nicman and Jiayin Deng and Yash Raj Singh and Alicja Różycka and Katarzyna Ewa Greber and Daniel Szulczyk and Michał Nowacki and Maciej Dawidowski and Anna Drabczyk and Jolanta Jaśkowska and Przemysław Zaręba and Damian Kułaga and Marek Staszewski and Monika Wujec and Piotr Krzeczyński and Olga Michalak and Greta Klejborowska and Camilla Scarpellini and Koen Augustyns and Jaka Dernovšek and Tihomir Tomašič and Jan Korabecny and Joanna Fedorowicz and Jarosław Sączewski and Wiesław Sawicki and Defang Ouyang and Krzesimir Ciura},
  year = {2026},
  journal = {Analytica Chimica Acta},
  doi = {10.1016/j.aca.2026.346201},
  url = {https://doi.org/10.1016/j.aca.2026.346201}
}

RIS

TY  - JOUR
TI  - Why choose between prediction power and mechanistic insight in QSRR modeling? A case study of artificial membrane chromatography and machine learning
AU  - Wiktor Nisterenko
AU  - Julia Nicman
AU  - Jiayin Deng
AU  - Yash Raj Singh
AU  - Alicja Różycka
AU  - Katarzyna Ewa Greber
AU  - Daniel Szulczyk
AU  - Michał Nowacki
AU  - Maciej Dawidowski
AU  - Anna Drabczyk
AU  - Jolanta Jaśkowska
AU  - Przemysław Zaręba
AU  - Damian Kułaga
AU  - Marek Staszewski
AU  - Monika Wujec
AU  - Piotr Krzeczyński
AU  - Olga Michalak
AU  - Greta Klejborowska
AU  - Camilla Scarpellini
AU  - Koen Augustyns
AU  - Jaka Dernovšek
AU  - Tihomir Tomašič
AU  - Jan Korabecny
AU  - Joanna Fedorowicz
AU  - Jarosław Sączewski
AU  - Wiesław Sawicki
AU  - Defang Ouyang
AU  - Krzesimir Ciura
PY  - 2026
JO  - Analytica Chimica Acta
DO  - 10.1016/j.aca.2026.346201
UR  - https://doi.org/10.1016/j.aca.2026.346201
ER  -

APA

Nisterenko, W., Nicman, J., Deng, J., Singh, Y. R., Różycka, A., Greber, K. E., Szulczyk, D., Nowacki, M., Dawidowski, M., Drabczyk, A., Jaśkowska, J., Zaręba, P., Kułaga, D., Staszewski, M., Wujec, M., Krzeczyński, P., Michalak, O., Klejborowska, G., Scarpellini, C., Augustyns, K., Dernovšek, J., Tomašič, T., Korabecny, J., Fedorowicz, J., Sączewski, J., Sawicki, W., Ouyang, D., & Ciura, K. (2026). Why choose between prediction power and mechanistic insight in QSRR modeling? A case study of artificial membrane chromatography and machine learning. Analytica Chimica Acta. https://doi.org/10.1016/j.aca.2026.346201

Source records

crossref · retrieved 2026-11-10T12:45:27.350Z