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OS02-05 In silico NAMs for nanoforms: how far we are?

Toxicology letters, 2024-09, Vol.399, p.S59-S59

Puzyn, T.

2024

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Global versus local QSAR models for predicting ionic liquids toxicity against IPC-81 leukemia rat cell line: The predictive ability

Journal of molecular liquids, 2017-04, Vol.231, p.333-340

2017

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An ancestral molecular response to nanomaterial particulates

Nature nanotechnology, 2023-08, Vol.18 (8), p.957-966

2023

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Rapid insight into C60 influence on biological functions of proteins

Structural chemistry, 2017-12, Vol.28 (6), p.1775-1788

2017

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Addressing a bottle neck for regulation of nanomaterials: quantitative read-across (Nano-QRA) algorithm for cases when only limited data is available

Environmental science. Nano, 2017-02, Vol.4 (2), p.346-358

2017

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Using the Isalos platform to develop a (Q)SAR model that predicts metal oxide toxicity utilizing facet-based electronic, image analysis-based, and periodic table derived properties as descriptors

Structural chemistry, 2022-04, Vol.33 (2), p.527-538

2022

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Evaluation criteria for the quality of published experimental data on nanomaterials and their usefulness for QSAR modelling

SAR and QSAR in environmental research, 2013-12, Vol.24 (12), p.995-1008

2013

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Chemometric analysis of correlations between electronic absorption characteristics and structural and/or physicochemical parameters for ampholytic substances of biological and pharmaceutical relevance

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy, 2015-03, Vol.138, p.700-710

2015

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Ab Initio Studies of Anatase TiO 2 (101) Surface-supported Au 8 Clusters

Current topics in medicinal chemistry, 2015-06, Vol.15 (18), p.1859-1867

2015

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Achievements and perspectives of computational nanotoxicology

Toxicology letters, 2015-10, Vol.238 (2), p.S34-S35

Puzyn, T.

2015

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On the uses of predictive toxicology to approve the use of engineered nanomaterials as biocidal active substances under the Biocidal Products Regulation

IOP conference series. Materials Science and Engineering, 2019-04, Vol.499 (1), p.12007

2019

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Estimating persistence of brominated and chlorinated organic pollutants in air, water, soil, and sediments with the QSPR-based classification scheme

Molecular diversity, 2011-02, Vol.15 (1), p.173-188

2011

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Computational Nanotoxicology: Challenges and Perspectives

1st, 2020

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Octanol/Water Partition Coefficients of Chloronaphthalenes

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering, 2005-09, Vol.40 (9), p.1651-1663

2005

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P19-83 Employing QSAR modeling for regulatory assessment of TiO2 nanoparticle safety

Toxicology letters, 2024-09, Vol.399, p.S287-S288

2024

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P05-07 Physicochemical properties and transcriptomic pathways analysis of TiO2 nanoparticles exposure through computer-aided approaches to provide a new öook at their pulmonary safety

Toxicology letters, 2024-09, Vol.399, p.S134-S134

2024

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Application and comparison of different chemometric approaches in QSPR modelling of supercooled liquid vapour pressures for chloronaphthalenes

SAR and QSAR in environmental research, 2007-05, Vol.18 (3-4), p.299-313

2007

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Application of QSAR modeling to predict the toxicity of nanomaterials - principles of obtaining reliable and valuable results

Toxicology letters, 2021-09, Vol.350, p.S87-S87

2021

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Concentrations of mercury in wild growing higher fungi and underlying substrate near Lake Wdzydze, Poland

Water, air, and soil pollution, 2003-09, Vol.148 (1-4), p.127-137

2003

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Computational Prediction of 7-Ethoxyresorufin-O-Diethylase (EROD) and Luciferase (luc) Inducing Potency for 75 Congeners of Chloronaphthalene

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering, 2004-12, Vol.39 (6), p.1505-1523

2004

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P06-02: Virtual screening of PFAS and their binding affinity to nuclear receptors – a structure-based approach

Toxicology letters, 2023-09, Vol.384, p.S102-S102

2023

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P05-14 Comprehensive in vitro/in silico approach for screening PFAS in terms of their potency of interfering with the thyroid system (TTR- TRβ CALUX assay)

Toxicology letters, 2024-09, Vol.399, p.S137-S138

2024

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Web application to predict the ‘Agranulocyte adhesion pathway and diapedesis’ in AOP 173 induced by MWCNTs

Toxicology letters, 2021-09, Vol.350, p.S83-S83

2021

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The differences in the 2D and 3D cytotoxicity testing results -an in silico approach

Toxicology letters, 2021-09, Vol.350, p.S85-S85

2021

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How to use AOP to prioritize NanoQSAR model development?

Toxicology letters, 2021-09, Vol.350, p.S82-S83

2021

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OS02-05 In silico NAMs for nanoforms: how far we are?

Global versus local QSAR models for predicting ionic liquids toxicity against IPC-81 leukemia rat cell line: The predictive ability

An ancestral molecular response to nanomaterial particulates

Rapid insight into C60 influence on biological functions of proteins

Addressing a bottle neck for regulation of nanomaterials: quantitative read-across (Nano-QRA) algorithm for cases when only limited data is available

Using the Isalos platform to develop a (Q)SAR model that predicts metal oxide toxicity utilizing facet-based electronic, image analysis-based, and periodic table derived properties as descriptors

Evaluation criteria for the quality of published experimental data on nanomaterials and their usefulness for QSAR modelling

Chemometric analysis of correlations between electronic absorption characteristics and structural and/or physicochemical parameters for ampholytic substances of biological and pharmaceutical relevance

Ab Initio Studies of Anatase TiO 2 (101) Surface-supported Au 8 Clusters

Achievements and perspectives of computational nanotoxicology

On the uses of predictive toxicology to approve the use of engineered nanomaterials as biocidal active substances under the Biocidal Products Regulation

Estimating persistence of brominated and chlorinated organic pollutants in air, water, soil, and sediments with the QSPR-based classification scheme

Computational Nanotoxicology: Challenges and Perspectives

Octanol/Water Partition Coefficients of Chloronaphthalenes

P19-83 Employing QSAR modeling for regulatory assessment of TiO2 nanoparticle safety

P05-07 Physicochemical properties and transcriptomic pathways analysis of TiO2 nanoparticles exposure through computer-aided approaches to provide a new öook at their pulmonary safety

Application and comparison of different chemometric approaches in QSPR modelling of supercooled liquid vapour pressures for chloronaphthalenes

Application of QSAR modeling to predict the toxicity of nanomaterials - principles of obtaining reliable and valuable results

Concentrations of mercury in wild growing higher fungi and underlying substrate near Lake Wdzydze, Poland

Computational Prediction of 7-Ethoxyresorufin-O-Diethylase (EROD) and Luciferase (luc) Inducing Potency for 75 Congeners of Chloronaphthalene

P06-02: Virtual screening of PFAS and their binding affinity to nuclear receptors – a structure-based approach

P05-14 Comprehensive in vitro/in silico approach for screening PFAS in terms of their potency of interfering with the thyroid system (TTR- TRβ CALUX assay)

Web application to predict the ‘Agranulocyte adhesion pathway and diapedesis’ in AOP 173 induced by MWCNTs

The differences in the 2D and 3D cytotoxicity testing results -an in silico approach

How to use AOP to prioritize NanoQSAR model development?

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