Author

W. Nisterenko

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Open access Sep 2026

Biomimetic Chromatography Profiling of a Neurotoxicology-Relevant Compound Panel: A Physicochemical Dataset for New Approach Methodologies

Developmental and adult neurotoxicity are hard to assess with conventional in vitro assays, which rarely account for how a chemical actually distributes once inside the body. Biomimetic chromatography offers a practical way to fill that gap, using stationary phases that mimic phospholipid membranes and major plasma pro...

W. Nisterenko, Y. R. Singh, K. Greber et al. · Biomimetics · 0 citations

Open access Sep 2026

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

Quantitative structure-retention relationship (QSRR) modeling of immobilized artificial membrane (IAM) chromatography was performed on a chemically diverse set of 2003 analytes to determine whether mechanistically interpretable, descriptor-based models can match the predictive performance of advanced machine-learning a...

W. Nisterenko, Julia Nicman, Jiayin Deng et al. · Analytica Chimica Acta · 0 citations

Open access Aug 2026

Revisiting the LSER Approach in the Era of Machine Learning: Insights from IAM Chromatography

The present study demonstrates the integration of the linear solvation energy relationship (LSER) concept with machine learning (ML) methodologies to improve the predictive and interpretative capabilities of chromatographic retention modeling. Immobilized artificial membrane (IAM) chromatography was employed as a mod...

W. Nisterenko, K. Greber, Magdalena Kierkowicz et al. · Molecular Pharmaceutics · 0 citations