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M. Gromelski · SciRah

Researcher profile · Semantic Scholar

M. Gromelski

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M. Gromelski — researcher profile on SciRah

h-index: 12
Works: 25
Citations: 433

Most-cited papers

Towards FAIR nanosafety data
N. Jeliazkova, M. Apostolova, C. Andreoli, F. Barone +34 more
2021Nature Nanotechnology99 citations Open access
Semantic Scholar
Nanomaterial grouping: Existing approaches and future recommendations
A. Giusti, Rambabu Atluri, R. Tsekovska, A. Gajewicz +27 more
2019NanoImpact54 citations Open access
Semantic Scholar
Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?
I. Lynch, A. Afantitis, Thomas E. Exner, M. Himly +26 more
2020Nanomaterials46 citations Open access
Semantic Scholar
Risk Governance of Emerging Technologies Demonstrated in Terms of its Applicability to Nanomaterials.
P. Isigonis, A. Afantitis, D. Antunes, A. Bartoňová +41 more
2020Small35 citations Open access
Semantic Scholar
The possibility to use multi-walled carbon nanotubes as a sorbent for dispersive solid phase extraction of selected pharmaceuticals and their metabolites: Effect of extraction condition
A. Jakubus, K. Godlewska, M. Gromelski, Karolina Jagiello +3 more
2019Microchemical journal (Print)34 citations
Semantic Scholar
Hazard identification of nanomaterials: In silico unraveling of descriptors for cytotoxicity and genotoxicity
N. el Yamani, E. Mariussen, M. Gromelski, E. Wyrzykowska +5 more
2022Nano Today26 citations Open access
Semantic Scholar
The FAIR AOP roadmap for 2025: Advancing findability, accessibility, interoperability, and re-usability of adverse outcome pathways
Holly M. Mortensen, M. Gromelski, G. Hench, Marvin Martens +11 more
2025Computational Toxicology23 citations Open access
Semantic Scholar
Quantitative multi-species toxicity modeling: Does a multi-species, machine learning model provide better performance than a single-species model for the evaluation of acute aquatic toxicity by organic pollutants?
Agnieszka Gajewicz-Skretna, E. Wyrzykowska, M. Gromelski
2022Science of the Total Environment23 citations
Semantic Scholar
Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models?
Agnieszka Gajewicz-Skretna, M. Gromelski, E. Wyrzykowska, A. Furuhama +2 more
2021Ecotoxicology and Environmental Safety20 citations Open access
Semantic Scholar
Dispersive solid-phase extraction using multi-walled carbon nanotubes combined with liquid chromatography–mass spectrometry for the analysis of β-blockers: Experimental and theoretical studies
A. Jakubus, M. Gromelski, Karolina Jagiello, T. Puzyn +2 more
2019Microchemical journal (Print)19 citations
Semantic Scholar
Why adverse outcome pathways need to be FAIR.
Clemens Wittwehr, Laure-Alix Clerbaux, Stephen W. Edwards, M. Angrish +8 more
2023ALTEX17 citations Open access
Semantic Scholar
Modeling of Anticancer Sulfonamide Derivatives Lipophilicity by Chemometric and Quantitative Structure-Retention Relationships Approaches
M. Pastewska, B. Żołnowska, Strahinja Z. Kovačević, Hanna Kapica +5 more
2022Molecules15 citations Open access
Semantic Scholar
AOP173 key event associated pathway predictor – online application for the prediction of benchmark dose lower bound (BMDLs) of a transcriptomic pathway involved in MWCNTs-induced lung fibrosis
M. Gromelski, F. Stoliński, Karolina Jagiello, A. Rybińska-Fryca +4 more
2022Nanotoxicology7 citations
Semantic Scholar
Hybrid Machine Learning and Experimental Studies of Antiviral Potential of Ionic Liquids against P100, MS2, and Phi6
Szymon Zdybel, A. Sosnowska, D. Kowalska, Julia Sommer +4 more
2024Journal of Chemical Information and Modeling6 citations
Semantic Scholar
NanoMixHamster: a web-based tool for predicting cytotoxicity of TiO2-based multicomponent nanomaterials toward Chinese hamster ovary (CHO-K1) cells
F. Stoliński, A. Rybińska-Fryca, M. Gromelski, A. Mikołajczyk +1 more
2022Nanotoxicology5 citations Open access
Semantic Scholar
Prediction of the retention factor in cetyltrimethylammonium bromide modified micellar electrokinetic chromatography using a machine learning approach
Krzesimir Ciura, Izabela Fryca, M. Gromelski
2023Microchemical journal (Print)4 citations
Semantic Scholar
Chemometric outlook on correlations between retention parameters of polar and semipolar HPLC columns and physicochemical characteristics of ampholytic substances of biological and pharmaceutical relevance
Urszula Judycka, Karolina Jagiello, M. Gromelski, L. Bober +2 more
2018Structural Chemistry2 citations Open access
Semantic Scholar
Implementation of the Grouping and Read-Across Approach for the Prediction of Nanomaterials Ecotoxicity
M. Gromelski, A. Rybińska-Fryca, M. Vijver, W. Peijnenburg +11 more
2022Social Science Research Network1 citations
Semantic Scholar
Chemometric approach to correlations between retention parameters of non-polar HPLC columns and physicochemical characteristics for ampholytic substances of biological and pharmaceutical relevance.
Urszula Judycka, Karolina Jagiello, M. Gromelski, L. Bober +2 more
2018Journal of chromatography. B, Analytical technologies in the biomedical and life sciences1 citations
Semantic Scholar
Can we integrate plasma protein binding data into IATA using new approach methodologies? A case study on bisphenol analogs.
Y. R. Singh, K. Greber, Szymon Zdybel, K. Bogusz +8 more
2026Chemico-Biological Interactions0 citations
Semantic Scholar
P18-25 Integrating Biomimetic Chromatography and In Silico Methods to Predict Plasma Protein Binding as IATA-driven SSbD concept for Development for Flame Retardant Alternative
K. Jagiello, Y. Singh, K. Bogusz, K. Greber +4 more
2025Toxicology Letters0 citations
Semantic Scholar
How do the existing read-across frameworks work for nanomaterials?
A. Rybińska-Fryca, M. Gromelski, M. Vijver, W. Peijnenburg +7 more
2021Toxicology Letters0 citations
Semantic Scholar
Application of QSAR modeling to predict the toxicity of nanomaterials - principles of obtaining reliable and valuable results
E. Wyrzykowska, A. Mikolajczyk, M. Gromelski, T. Puzyn
2021Toxicology Letters0 citations
Semantic Scholar
In silico unravelling of descriptors for cytotoxicity and genotoxicity for hazard identification of nanomaterials
N. el Yamani, M. Gromelski, E. Mariussen, E. Wyrzykowska +4 more
2021Toxicology Letters0 citations
Semantic Scholar
Web application to predict the ‘Agranulocyte adhesion pathway and diapedesis’ in AOP 173 induced by MWCNTs
M. Gromelski, F. Stoliński, A. Rybińska-Fryca, K. Jagiello +1 more
2021Toxicology Letters0 citations
Semantic Scholar

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