N Sizochenko
N Sizochenko
Other namesNatalia Novoselska
PICompS founder || Ronin scholar
Verified email at
Cited by
Cited by
From basic physics to mechanisms of toxicity: The “liquid drop” approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles
N Sizochenko, B Rasulev, A Gajewicz, V Kuz'min, T Puzyn, J Leszczynski
Nanoscale 6 (22), 13986-13993, 2014
How the toxicity of nanomaterials towards different species could be simultaneously evaluated: A novel multi-nano-read-across approach
N Sizochenko, A Mikolajczyk, K Jagiello, T Puzyn, J Leszczynski, ...
Nanoscale 10, 582-591, 2018
Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques
N Sizochenko, M Syzochenko, N Fjodorova, B Rasulev, J Leszczynski
Ecotoxicology and environmental safety 185, 109733, 2019
Chemoinformatics approach for characterization of hybrid nanomaterials: safer and efficient design perspective
A Mikolajczyk, N Sizochenko, E Mulkiewicz, A Malankowska, B Rasulev, ...
Nanoscale 11, 11808-11818, 2019
Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and predictive neural networks modeling
N Sizochenko, A Mikolajczyk, M Syzochenko, T Puzyn, J Leszczynski
NanoImpact 22, 100317, 2021
Rutin as promising drug for the treatment of Parkinson’s disease: an assessment of MAO-B inhibitory potential by docking, molecular dynamics and DFT studies
F Azam, HS Abodabos, IM Taban, AR Rfieda, D Mahmood, MJ Anwar, ...
Molecular Simulation 45 (18), 1563-1571, 2019
Towards the development of global nano-quantitative structure–property relationship models: zeta potentials of metal oxide nanoparticles
AA Toropov, N Sizochenko, AP Toropova, J Leszczynski
Nanomaterials 8 (4), 243, 2018
In vivo toxicity of nitroaromatics: A comprehensive quantitative structure–activity relationship study
A Gooch, N Sizochenko, B Rasulev, L Gorb, J Leszczynski
Environmental Toxicology and Chemistry 36 (8), 2227–2233, 2017
Evaluating the toxicity of TiO2-based nanoparticles to Chinese hamster ovary cells and Escherichia coli: A complementary experimental and computational approach
A Mikolajczyk, N Sizochenko, E Mulkiewicz, A Malankowska, M Nischk, ...
Beilstein Journal of Nanotechnology 8, 2171, 2017
Quantitative structure-property relationship model leading to virtual screening of fullerene derivatives: Exploring structural attributes critical for photoconversion …
S Kar, N Sizochenko, L Ahmed, VS Batista, J Leszczynski
Nano Energy 26, 677-691, 2016
Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models
N Sizochenko, A Gajewicz, J Leszczynski, T Puzyn
Nanoscale 8 (13), 7203-7208, 2016
Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties
A Golbamaki, N Golbamaki, N Sizochenko, B Rasulev, J Leszczynski, ...
Nanotoxicology 12 (10), 1113-1129, 2018
Review of current and emerging approaches for quantitative nanostructure-activity relationship modeling: The case of inorganic nanoparticles
N Sizochenko, J Leszczynski
Journal of Nanotoxicology and Nanomedicine 1 (1), 1-16, 2016
Predicting physical properties of nanofluids by computational modeling
N Sizochenko, M Syzochenko, A Gajewicz, J Leszczynski, T Puzyn
The Journal of Physical Chemistry C 121 (3), 1910–1917, 2017
A DFT-based QSAR study on inhibition of human dihydrofolate reductase
S Karabulut, N Sizochenko, A Orhan, J Leszczynski
Journal of Molecular Graphics and Modelling 70, 23-29, 2016
Modeling of interactions between the zebrafish hatching enzyme ZHE1 and a series of metal oxide nanoparticles: nano-QSAR and causal analysis of inactivation mechanisms
N Sizochenko, D Leszczynska, J Leszczynski
Nanomaterials 7 (10), 330, 2017
Causal inference methods to assist in mechanistic interpretation of classification nano-SAR models
N Sizochenko, B Rasulev, A Gajewicz, E Mokshyna, VE Kuz'min, ...
RSC Advances 5 (95), 77739-77745, 2015
How the “liquid drop” approach could be efficiently applied for quantitative structure–property relationship modeling of nanofluids
N Sizochenko, K Jagiello, J Leszczynski, T Puzyn
The Journal of Physical Chemistry C 119 (45), 25542-25547, 2015
Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions
AP Toropova, AA Toropov, J Leszczynski, N Sizochenko
Environmental Toxicology and Pharmacology 86, 103665, 2021
Advancement of predictive modeling of zeta potentials (ζ) in metal oxide nanoparticles with correlation intensity index (CII)
AA Toropov, N Sizochenko, AP Toropova, D Leszczynska, J Leszczynski
Journal of Molecular Liquids 317, 113929, 2020
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