| Year |
Citation |
Score |
| 2024 |
Varsou DD, Banerjee A, Roy J, Roy K, Savvas G, Sarimveis H, Wyrzykowska E, Balicki M, Puzyn T, Melagraki G, Lynch I, Afantitis A. The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential. Beilstein Journal of Nanotechnology. 15: 1536-1553. PMID 39624206 DOI: 10.3762/bjnano.15.121 |
0.313 |
|
| 2023 |
Furxhi I, Kalapus M, Costa A, Puzyn T. Artificial augmented dataset for the enhancement of nano-QSARs models. A methodology based on topological projections. Nanotoxicology. 1-16. PMID 37885250 DOI: 10.1080/17435390.2023.2268163 |
0.33 |
|
| 2023 |
Sengottiyan S, Mikolajczyk A, Jagiełło K, Swirog M, Puzyn T. Core, Coating, or Corona? The Importance of Considering Protein Coronas in nano-QSPR Modeling of Zeta Potential. Acs Nano. PMID 36651824 DOI: 10.1021/acsnano.2c06977 |
0.301 |
|
| 2022 |
Swirog M, Mikolajczyk A, Jagiello K, Jänes J, Tämm K, Puzyn T. Predicting electrophoretic mobility of TiO, ZnO, and CeO nanoparticles in natural waters: The importance of environment descriptors in nanoinformatics models. The Science of the Total Environment. 840: 156572. PMID 35710003 DOI: 10.1016/j.scitotenv.2022.156572 |
0.375 |
|
| 2021 |
Sizochenko N, Mikolajczyk A, Syzochenko M, Puzyn T, Leszczynski J. Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling. Nanoimpact. 22: 100317. PMID 35559974 DOI: 10.1016/j.impact.2021.100317 |
0.397 |
|
| 2021 |
Sizochenko N, Mikolajczyk A, Syzochenko M, Puzyn T, Leszczynski J. Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling Nanoimpact. 22: 100317. DOI: 10.1016/J.IMPACT.2021.100317 |
0.315 |
|
| 2020 |
Rybińska-Fryca A, Mikolajczyk A, Puzyn T. Structure-activity prediction networks (SAPNets): a step beyond Nano-QSAR for effective implementation of the safe-by-design concept. Nanoscale. 12: 20669-20676. PMID 33048104 DOI: 10.1039/d0nr05220e |
0.321 |
|
| 2020 |
Rybińska-Fryca A, Sosnowska A, Puzyn T. Representation of the Structure-A Key Point of Building QSAR/QSPR Models for Ionic Liquids. Materials (Basel, Switzerland). 13. PMID 32486309 DOI: 10.3390/Ma13112500 |
0.371 |
|
| 2020 |
Federico A, Serra A, Ha MK, Kohonen P, Choi JS, Liampa I, Nymark P, Sanabria N, Cattelani L, Fratello M, Kinaret PAS, Jagiello K, Puzyn T, Melagraki G, Gulumian M, et al. Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data. Nanomaterials (Basel, Switzerland). 10. PMID 32397130 DOI: 10.3390/Nano10050903 |
0.339 |
|
| 2020 |
Kinaret PAS, Serra A, Federico A, Kohonen P, Nymark P, Liampa I, Ha MK, Choi JS, Jagiello K, Sanabria N, Melagraki G, Cattelani L, Fratello M, Sarimveis H, Afantitis A, ... ... Puzyn T, et al. Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects. Nanomaterials (Basel, Switzerland). 10. PMID 32326418 DOI: 10.3390/Nano10040750 |
0.375 |
|
| 2020 |
Serra A, Fratello M, Cattelani L, Liampa I, Melagraki G, Kohonen P, Nymark P, Federico A, Kinaret PAS, Jagiello K, Ha MK, Choi JS, Sanabria N, Gulumian M, Puzyn T, et al. Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment. Nanomaterials (Basel, Switzerland). 10. PMID 32276469 DOI: 10.3390/Nano10040708 |
0.391 |
|
| 2020 |
Rybińska-Fryca A, Mikolajczyk A, Łuczak J, Paszkiewicz-Gawron M, Paszkiewicz M, Zaleska-Medynska A, Puzyn T. How thermal stability of ionic liquids leads to more efficient TiO-based nanophotocatalysts: Theoretical and experimental studies. Journal of Colloid and Interface Science. 572: 396-407. PMID 32272314 DOI: 10.1016/J.Jcis.2020.03.079 |
0.31 |
|
| 2020 |
Afantitis A, Melagraki G, Isigonis P, Tsoumanis A, Varsou DD, Valsami-Jones E, Papadiamantis A, Ellis LA, Sarimveis H, Doganis P, Karatzas P, Tsiros P, Liampa I, Lobaskin V, Greco D, ... ... Puzyn T, et al. NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for nanosafety assessment. Computational and Structural Biotechnology Journal. 18: 583-602. PMID 32226594 DOI: 10.1016/J.Csbj.2020.02.023 |
0.374 |
|
| 2019 |
Barabaś A, Jagiełło K, Rybińska-Fryca A, Dąbrowska AM, Puzyn T. How the configurational changes influence on molecular characteristics. The alkyl 3-azido-2,3-dideoxy-D-hexopyranosides - Theoretical approach. Carbohydrate Research. 481: 72-79. PMID 31254910 DOI: 10.1016/J.Carres.2019.06.012 |
0.315 |
|
| 2019 |
Mikolajczyk A, Sizochenko N, Mulkiewicz E, Malankowska A, Rasulev B, Puzyn T. A chemoinformatics approach for the characterization of hybrid nanomaterials: safer and efficient design perspective. Nanoscale. 11: 11808-11818. PMID 31184677 DOI: 10.1039/C9Nr01162E |
0.672 |
|
| 2019 |
Acharya K, Werner D, Dolfing J, Barycki M, Meynet P, Mrozik W, Komolafe O, Puzyn T, Davenport RJ. A quantitative structure-biodegradation relationship (QSBR) approach to predict biodegradation rates of aromatic chemicals. Water Research. 157: 181-190. PMID 30953853 DOI: 10.1016/J.Watres.2019.03.086 |
0.35 |
|
| 2019 |
Sosnowska A, Brzeski J, Skurski P, Puzyn T. The Acid Strength of the Lewis-Brønsted Superacids - A QSPR Study. Molecular Informatics. 38: e1800113. PMID 30747480 DOI: 10.1002/Minf.201800113 |
0.322 |
|
| 2019 |
Wyrzykowska E, Rybińska-Fryca A, Sosnowska A, Puzyn T. Virtual screening in the design of ionic liquids as environmentally safe bactericides Green Chemistry. 21: 1965-1973. DOI: 10.1039/C8Gc03400A |
0.305 |
|
| 2019 |
Giusti A, Atluri R, Tsekovska R, Gajewicz A, Apostolova MD, Battistelli CL, Bleeker EAJ, Bossa C, Bouillard J, Dusinska M, Gómez-Fernández P, Grafström R, Gromelski M, Handzhiyski Y, Jacobsen NR, ... ... Puzyn T, et al. Nanomaterial grouping: Existing approaches and future recommendations Nanoimpact. 16: 100182. DOI: 10.1016/J.Impact.2019.100182 |
0.388 |
|
| 2018 |
Oberbek P, Bolek T, Chlanda A, Hirano S, Kusnieruk S, Rogowska-Tylman J, Nechyporenko G, Zinchenko V, Swieszkowski W, Puzyn T. Characterization and influence of hydroxyapatite nanopowders on living cells. Beilstein Journal of Nanotechnology. 9: 3079-3094. PMID 30643706 DOI: 10.3762/Bjnano.9.286 |
0.391 |
|
| 2018 |
Barycki M, Sosnowska A, Jagiello K, Puzyn T. Multi-Objective Genetic Algorithm (MOGA) As a Feature Selecting Strategy in the Development of Ionic Liquids' Quantitative Toxicity-Toxicity Relationship Models. Journal of Chemical Information and Modeling. 58: 2467-2476. PMID 30507178 DOI: 10.1021/Acs.Jcim.8B00378 |
0.42 |
|
| 2018 |
Judycka U, Jagiello K, Gromelski M, Bober L, Błażejowski J, Puzyn T. Chemometric approach to correlations between retention parameters of non-polar HPLC columns and physicochemical characteristics for ampholytic substances of biological and pharmaceutical relevance. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences. 1095: 8-14. PMID 30036737 DOI: 10.1016/J.Jchromb.2018.07.019 |
0.365 |
|
| 2018 |
Sosnowska A, Rybinska-Fryca A, Barycki M, Jagiello K, Puzyn T. Chemoinformatic Approach to Assess Toxicity of Ionic Liquids. Methods in Molecular Biology (Clifton, N.J.). 1800: 559-571. PMID 29934911 DOI: 10.1007/978-1-4939-7899-1_26 |
0.356 |
|
| 2018 |
Ambure P, Bhat J, Puzyn T, Roy K. Identifying natural compounds as multi-target directed ligands against Alzheimer's disease: an in silico approach. Journal of Biomolecular Structure & Dynamics. 1-47. PMID 29578387 DOI: 10.1080/07391102.2018.1456975 |
0.345 |
|
| 2018 |
Sizochenko N, Mikolajczyk A, Jagiello K, Puzyn T, Leszczynski J, Rasulev B. How the toxicity of nanomaterials towards different species could be simultaneously evaluated: a novel multi-nano-read-across approach. Nanoscale. 10: 582-591. PMID 29168526 DOI: 10.1039/C7Nr05618D |
0.691 |
|
| 2018 |
Mikolajczyk A, Gajewicz A, Mulkiewicz E, Rasulev B, Marchelek M, Diak M, Hirano S, Zaleska-Medynska A, Puzyn T. Nano-QSAR modeling for ecosafe design of heterogeneous TiO2-based nano-photocatalysts Environmental Science. Nano. 5: 1150-1160. DOI: 10.1039/C8En00085A |
0.658 |
|
| 2018 |
Rybinska-Fryca A, Sosnowska A, Puzyn T. Prediction of dielectric constant of ionic liquids Journal of Molecular Liquids. 260: 57-64. DOI: 10.1016/J.Molliq.2018.03.080 |
0.369 |
|
| 2018 |
Judycka U, Jagiełło K, Bober L, Błażejowski J, Puzyn T. Assessing therapeutic relevance of biologically interesting, ampholytic substances based on their physicochemical and spectral characteristics with chemometric tools Chemical Physics Letters. 701: 58-64. DOI: 10.1016/J.Cplett.2018.04.040 |
0.408 |
|
| 2018 |
Judycka U, Jagiello K, Gromelski M, Bober L, Błażejowski J, Puzyn T. Chemometric outlook on correlations between retention parameters of polar and semipolar HPLC columns and physicochemical characteristics of ampholytic substances of biological and pharmaceutical relevance Structural Chemistry. 29: 1839-1844. DOI: 10.1007/S11224-018-1174-5 |
0.333 |
|
| 2018 |
Jagiello K, Makurat S, Pereć S, Rak J, Puzyn T. Molecular features of thymidine analogues governing the activity of human thymidine kinase Structural Chemistry. 29: 1367-1374. DOI: 10.1007/S11224-018-1124-2 |
0.316 |
|
| 2017 |
Gajewicz A, Puzyn T, Odziomek K, Urbaszek P, Haase A, Riebeling C, Luch A, Irfan MA, Landsiedel R, van der Zande M, Bouwmeester H. Decision tree models to classify nanomaterials according to the DF4nanoGrouping scheme. Nanotoxicology. 1-17. PMID 29251527 DOI: 10.1080/17435390.2017.1415388 |
0.418 |
|
| 2017 |
Stone V, Führ M, Feindt PH, Bouwmeester H, Linkov I, Sabella S, Murphy F, Bizer K, Tran L, Ågerstrand M, Fito C, Andersen T, Anderson D, Bergamaschi E, Cherrie JW, ... ... Puzyn T, et al. The Essential Elements of a Risk Governance Framework for Current and Future Nanotechnologies. Risk Analysis : An Official Publication of the Society For Risk Analysis. PMID 29240986 DOI: 10.1111/Risa.12954 |
0.309 |
|
| 2017 |
Mikolajczyk A, Sizochenko N, Mulkiewicz E, Malankowska A, Nischk M, Jurczak P, Hirano S, Nowaczyk G, Zaleska-Medynska A, Leszczynski J, Gajewicz A, Puzyn T. Evaluating the toxicity of TiO2-based nanoparticles to Chinese hamster ovary cells and Escherichia coli: a complementary experimental and computational approach. Beilstein Journal of Nanotechnology. 8: 2171-2180. PMID 29114443 DOI: 10.3762/Bjnano.8.216 |
0.474 |
|
| 2017 |
Puzyn T, Jeliazkova N, Sarimveis H, Marchese Robinson RL, Lobaskin V, Rallo R, Richarz AN, Gajewicz A, Papadopulos MG, Hastings J, Cronin MTD, Benfenati E, Fernandez A. Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology. Food and Chemical Toxicology : An International Journal Published For the British Industrial Biological Research Association. PMID 28943385 DOI: 10.1016/J.Fct.2017.09.037 |
0.441 |
|
| 2017 |
Bañares MA, Haase A, Tran L, Lobaskin V, Oberdörster G, Rallo R, Leszczynski J, Hoet P, Korenstein R, Hardy B, Puzyn T. CompNanoTox2015: novel perspectives from a European conference on computational nanotoxicology on predictive nanotoxicology. Nanotoxicology. 1-7. PMID 28885075 DOI: 10.1080/17435390.2017.1371351 |
0.4 |
|
| 2017 |
Urbaszek P, Gajewicz A, Sikorska C, Haranczyk M, Puzyn T. Modeling adsorption of brominated, chlorinated and mixed bromo/chloro-dibenzo-p-dioxins on C60 fullerene using Nano-QSPR. Beilstein Journal of Nanotechnology. 8: 752-761. PMID 28487818 DOI: 10.3762/Bjnano.8.78 |
0.325 |
|
| 2017 |
Richarz AN, Avramopoulos A, Benfenati E, Gajewicz A, Golbamaki Bakhtyari N, Leonis G, Marchese Robinson RL, Papadopoulos MG, Cronin MT, Puzyn T. Compilation of Data and Modelling of Nanoparticle Interactions and Toxicity in the NanoPUZZLES Project. Advances in Experimental Medicine and Biology. 947: 303-324. PMID 28168672 DOI: 10.1007/978-3-319-47754-1_10 |
0.504 |
|
| 2017 |
Mikolajczyk A, Nadolna J, Zalewska-Medynska A, Puzyn T. Combined Experimental and Computational Approach to Develop Efficient Photocatalysts Based on RE-TiO2 Nanoparticles Ran. DOI: 10.11159/Icnms17.107 |
0.398 |
|
| 2017 |
Gajewicz A, Jagiello K, Cronin MTD, Leszczynski J, Puzyn T. 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. 4: 346-358. DOI: 10.1039/C6En00399K |
0.329 |
|
| 2017 |
Sizochenko N, Syzochenko M, Gajewicz A, Leszczynski J, Puzyn T. Predicting Physical Properties of Nanofluids by Computational Modeling Journal of Physical Chemistry C. 121: 1917. DOI: 10.1021/Acs.Jpcc.6B08850 |
0.363 |
|
| 2017 |
Sosnowska A, Grzonkowska M, Puzyn T. Global versus local QSAR models for predicting ionic liquids toxicity against IPC-81 leukemia rat cell line: The predictive ability Journal of Molecular Liquids. 231: 333-340. DOI: 10.1016/J.Molliq.2017.02.025 |
0.405 |
|
| 2017 |
Giełdoń A, Witt MM, Gajewicz A, Puzyn T. Rapid insight into C60 influence on biological functions of proteins Structural Chemistry. 28: 1775-1788. DOI: 10.1007/S11224-017-0957-4 |
0.362 |
|
| 2016 |
Cassano A, Marchese Robinson RL, Palczewska A, Puzyn T, Gajewicz A, Tran L, Manganelli S, Cronin MT. Comparing the CORAL and Random Forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials. Alternatives to Laboratory Animals : Atla. 44: 533-556. PMID 28094535 DOI: 10.1177/026119291604400603 |
0.396 |
|
| 2016 |
Wyrzykowska E, Mikolajczyk A, Sikorska C, Puzyn T. Development of a novel in silico model of zeta potential for metal oxide nanoparticles: a nano-QSPR approach. Nanotechnology. 27: 445702. PMID 27668939 DOI: 10.1088/0957-4484/27/44/445702 |
0.498 |
|
| 2016 |
Jagiello K, Grzonkowska M, Swirog M, Ahmed L, Rasulev B, Avramopoulos A, Papadopoulos MG, Leszczynski J, Puzyn T. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives. Journal of Nanoparticle Research : An Interdisciplinary Forum For Nanoscale Science and Technology. 18: 256. PMID 27642255 DOI: 10.1007/S11051-016-3564-1 |
0.67 |
|
| 2016 |
Grzonkowska M, Sosnowska A, Barycki M, Rybinska A, Puzyn T. How the structure of ionic liquid affects its toxicity to Vibrio fischeri? Chemosphere. 159: 199-207. PMID 27295436 DOI: 10.1016/J.Chemosphere.2016.06.004 |
0.39 |
|
| 2016 |
Marchese Robinson RL, Lynch I, Peijnenburg W, Rumble J, Klaessig F, Marquardt C, Rauscher H, Puzyn T, Purian R, Åberg C, Karcher S, Vriens H, Hoet P, Hoover MD, Hendren CO, et al. How should the completeness and quality of curated nanomaterial data be evaluated? Nanoscale. PMID 27143028 DOI: 10.1039/C5Nr08944A |
0.309 |
|
| 2016 |
Sizochenko N, Gajewicz A, Leszczynski J, Puzyn T. Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models. Nanoscale. 8: 7203-8. PMID 26972917 DOI: 10.1039/C5Nr08279J |
0.442 |
|
| 2016 |
Sosnowska A, Barycki M, Gajewicz A, Bobrowski M, Freza S, Skurski P, Uhl S, Laux E, Journot T, Jeandupeux L, Keppner H, Puzyn T. Towards the Application of Structure-Property Relationship Modeling in Materials Science: Predicting the Seebeck Coefficient for Ionic Liquid/Redox Couple Systems. Chemphyschem : a European Journal of Chemical Physics and Physical Chemistry. 17: 1591-600. PMID 26919483 DOI: 10.1002/Cphc.201600080 |
0.317 |
|
| 2016 |
Rybinska A, Sosnowska A, Barycki M, Puzyn T. Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids. Journal of Computer-Aided Molecular Design. 30: 165-76. PMID 26830600 DOI: 10.1007/S10822-016-9894-3 |
0.387 |
|
| 2016 |
Kar S, Gajewicz A, Roy K, Leszczynski J, Puzyn T. Extrapolating between toxicity endpoints of metal oxide nanoparticles: Predicting toxicity to Escherichia coli and human keratinocyte cell line (HaCaT) with Nano-QTTR. Ecotoxicology and Environmental Safety. 126: 238-244. PMID 26773833 DOI: 10.1016/J.Ecoenv.2015.12.033 |
0.706 |
|
| 2016 |
Rybinska A, Sosnowska A, Grzonkowska M, Barycki M, Puzyn T. Filling environmental data gaps with QSPR for ionic liquids: Modeling n-octanol/water coefficient. Journal of Hazardous Materials. 303: 137-44. PMID 26530890 DOI: 10.1016/J.Jhazmat.2015.10.023 |
0.345 |
|
| 2016 |
Mikolajczyk A, Malankowska A, Nowaczyk G, Gajewicz A, Hirano S, Jurga S, Zaleska-Medynska A, Puzyn T. Combined experimental and computational approach to developing efficient photocatalysts based on Au/Pd–TiO2 nanoparticles Environmental Science. Nano. 3: 1425-1435. DOI: 10.1039/C6En00232C |
0.433 |
|
| 2016 |
Golbamaki A, Golbamaki N, Sizochenko N, Rasulev B, Cassano A, Puzyn T, Leszczynski J, Benfenati E. P17-030Classification nano-SAR modeling of metal oxides nanoparticles genotoxicity based on comet assay data Toxicology Letters. 258. DOI: 10.1016/J.Toxlet.2016.06.1950 |
0.654 |
|
| 2016 |
Barycki M, Sosnowska A, Gajewicz A, Bobrowski M, Wileńska D, Skurski P, Giełdoń A, Czaplewski C, Uhl S, Laux E, Journot T, Jeandupeux L, Keppner H, Puzyn T. Temperature-dependent structure-property modeling of viscosity for ionic liquids Fluid Phase Equilibria. 427: 9-17. DOI: 10.1016/J.Fluid.2016.06.043 |
0.315 |
|
| 2016 |
Sikorska C, Gajewicz A, Urbaszek P, Lubinski L, Puzyn T. Efficient way of designing fullerene derivatives based on simplified DFT calculations and QSPR modeling Chemometrics and Intelligent Laboratory Systems. 152: 125-133. DOI: 10.1016/J.Chemolab.2016.02.003 |
0.392 |
|
| 2016 |
Jagiello K, Chomicz B, Avramopoulos A, Gajewicz A, Mikolajczyk A, Bonifassi P, Papadopoulos MG, Leszczynski J, Puzyn T. Size-dependent electronic properties of nanomaterials: How this novel class of nanodescriptors supposed to be calculated? Structural Chemistry. 28: 635-643. DOI: 10.1007/S11224-016-0838-2 |
0.315 |
|
| 2015 |
Sikorska C, Puzyn T. The performance of selected semi-empirical and DFT methods in studying C₆₀ fullerene derivatives. Nanotechnology. 26: 455702. PMID 26472593 DOI: 10.1088/0957-4484/26/45/455702 |
0.302 |
|
| 2015 |
Judycka-Proma U, Bober L, Gajewicz A, Puzyn T, Błażejowski J. 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. 138: 700-710. PMID 25544186 DOI: 10.1016/J.Saa.2014.11.067 |
0.342 |
|
| 2015 |
Gajewicz A, Cronin MT, Rasulev B, Leszczynski J, Puzyn T. Novel approach for efficient predictions properties of large pool of nanomaterials based on limited set of species: nano-read-across. Nanotechnology. 26: 015701. PMID 25473798 DOI: 10.1088/0957-4484/26/1/015701 |
0.695 |
|
| 2015 |
Tantra R, Oksel C, Puzyn T, Wang J, Robinson KN, Wang XZ, Ma CY, Wilkins T. Nano(Q)SAR: Challenges, pitfalls and perspectives. Nanotoxicology. 9: 636-42. PMID 25211549 DOI: 10.3109/17435390.2014.952698 |
0.343 |
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| 2015 |
Gajewicz A, Schaeublin N, Rasulev B, Hussain S, Leszczynska D, Puzyn T, Leszczynski J. Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: hints from nano-QSAR studies. Nanotoxicology. 9: 313-25. PMID 24983896 DOI: 10.3109/17435390.2014.930195 |
0.664 |
|
| 2015 |
Sizochenko N, Rasulev B, Gajewicz A, Mokshyna E, Kuz'min VE, Leszczynski J, Puzyn T. Causal inference methods to assist in mechanistic interpretation of classification nano-SAR models Rsc Advances. 5: 77739-77745. DOI: 10.1039/C5Ra11399G |
0.697 |
|
| 2015 |
Mikolajczyk A, Gajewicz A, Rasulev B, Schaeublin N, Maurer-Gardner E, Hussain S, Leszczynski J, Puzyn T. Zeta potential for metal oxide nanoparticles: A predictive model developed by a nano-quantitative structure-property relationship approach Chemistry of Materials. 27: 2400-2407. DOI: 10.1021/Cm504406A |
0.69 |
|
| 2015 |
Sizochenko N, Jagiello K, Leszczynski J, Puzyn T. How the “Liquid Drop” Approach Could Be Efficiently Applied for Quantitative Structure–Property Relationship Modeling of Nanofluids Journal of Physical Chemistry C. 119: 25542-25547. DOI: 10.1021/Acs.Jpcc.5B05759 |
0.428 |
|
| 2015 |
Richarz A, Madden JC, Robinson RLM, Lubiński Ł, Mokshina E, Urbaszek P, Kuz׳min VE, Puzyn T, Cronin MTD. Development of computational models for the prediction of the toxicity of nanomaterials Perspectives On Science. 3: 27-29. DOI: 10.1016/J.Pisc.2014.11.015 |
0.381 |
|
| 2015 |
Jagiello K, Mostrag-Szlichtyng A, Gajewicz A, Kawai T, Imaizumi Y, Sakurai T, Yamamoto H, Tatarazako N, Mizukawa K, Aoki Y, Suzuki N, Watanabe H, Puzyn T. Towards modelling of the environmental fate of pharmaceuticals using the QSPR-MM scheme Environmental Modelling and Software. 72: 147-154. DOI: 10.1016/J.Envsoft.2015.06.013 |
0.412 |
|
| 2015 |
Ambure P, Aher RB, Gajewicz A, Puzyn T, Roy K. "NanoBRIDGES" software: Open access tools to perform QSAR and nano-QSAR modeling Chemometrics and Intelligent Laboratory Systems. 147: 1-13. DOI: 10.1016/J.Chemolab.2015.07.007 |
0.385 |
|
| 2014 |
Sizochenko N, Rasulev B, Gajewicz A, Kuz'min V, Puzyn T, Leszczynski J. From basic physics to mechanisms of toxicity: the "liquid drop" approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles. Nanoscale. 6: 13986-93. PMID 25317542 DOI: 10.1039/C4Nr03487B |
0.699 |
|
| 2014 |
Toropova AP, Toropov AA, Benfenati E, Puzyn T, Leszczynska D, Leszczynski J. Optimal descriptor as a translator of eclectic information into the prediction of membrane damage: the case of a group of ZnO and TiO2 nanoparticles. Ecotoxicology and Environmental Safety. 108: 203-9. PMID 25086232 DOI: 10.1016/J.Ecoenv.2014.07.005 |
0.39 |
|
| 2014 |
Kar S, Gajewicz A, Puzyn T, Roy K, Leszczynski J. Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach. Ecotoxicology and Environmental Safety. 107: 162-9. PMID 24949897 DOI: 10.1016/J.Ecoenv.2014.05.026 |
0.709 |
|
| 2014 |
Kawai T, Jagiello K, Sosnowska A, Odziomek K, Gajewicz A, Handoh IC, Puzyn T, Suzuki N. A new metric for long-range transport potential of chemicals. Environmental Science & Technology. 48: 3245-52. PMID 24579696 DOI: 10.1021/Es4026003 |
0.396 |
|
| 2014 |
Kar S, Gajewicz A, Puzyn T, Roy K. Nano-quantitative structure-activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells. Toxicology in Vitro : An International Journal Published in Association With Bibra. 28: 600-6. PMID 24412539 DOI: 10.1016/J.Tiv.2013.12.018 |
0.698 |
|
| 2014 |
Lubinski L, Urbaszek P, Gajewicz A, Cronin MT, Enoch SJ, Madden JC, Leszczynska D, Leszczynski J, Puzyn T. Evaluation criteria for the quality of published experimental data on nanomaterials and their usefulness for QSAR modelling. Sar and Qsar in Environmental Research. 24: 995-1008. PMID 24313439 DOI: 10.1080/1062936X.2013.840679 |
0.482 |
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| 2014 |
Sosnowska A, Barycki M, Zaborowska M, Rybinska A, Puzyn T. Towards designing environmentally safe ionic liquids: the influence of the cation structure Green Chemistry. 16: 4749-4757. DOI: 10.1039/C4Gc00526K |
0.355 |
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| 2014 |
Sosnowska A, Barycki M, Jagiello K, Haranczyk M, Gajewicz A, Kawai T, Suzuki N, Puzyn T. Predicting enthalpy of vaporization for Persistent Organic Pollutants with Quantitative Structure–Property Relationship (QSPR) incorporating the influence of temperature on volatility Atmospheric Environment. 87: 10-18. DOI: 10.1016/J.Atmosenv.2013.12.036 |
0.375 |
|
| 2014 |
Jagiello K, Sosnowska A, Walker S, Haranczyk M, Gajewicz A, Kawai T, Suzuki N, Leszczynski J, Puzyn T. Direct QSPR: the most efficient way of predicting organic carbon/water partition coefficient (log K OC) for polyhalogenated POPs Structural Chemistry. 25: 997-1004. DOI: 10.1007/S11224-014-0419-1 |
0.331 |
|
| 2013 |
Toropov AA, Toropova AP, Puzyn T, Benfenati E, Gini G, Leszczynska D, Leszczynski J. QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells. Chemosphere. 92: 31-7. PMID 23566368 DOI: 10.1016/J.Chemosphere.2013.03.012 |
0.43 |
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| 2013 |
Richarz A, Cronin M, Madden J, Lubinski L, Mokshina E, Urbaszek P, Puzyn T, Kuz’min V. Toxicity of nanomaterials: Availability and suitability of data for the development of in silico models Toxicology Letters. 221. DOI: 10.1016/J.Toxlet.2013.05.609 |
0.398 |
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| 2013 |
Odziomek K, Gajewicz A, Haranczyk M, Puzyn T. Reliability of environmental fate modeling results for POPs based on various methods of determining the air/water partition coefficient (log KAW) Atmospheric Environment. 73: 177-184. DOI: 10.1016/J.Atmosenv.2013.02.052 |
0.375 |
|
| 2013 |
Toropova AP, Toropov AA, Puzyn T, Benfenati E, Leszczynska D, Leszczynski J. Optimal descriptor as a translator of eclectic information into the prediction of thermal conductivity of micro-electro-mechanical systems Journal of Mathematical Chemistry. 51: 2230-2237. DOI: 10.1007/S10910-013-0211-2 |
0.349 |
|
| 2012 |
Haranczyk M, Urbaszek P, Ng EG, Puzyn T. Combinatorial × computational × cheminformatics (C3) approach to characterization of congeneric libraries of organic pollutants. Journal of Chemical Information and Modeling. 52: 2902-9. PMID 23036090 DOI: 10.1021/Ci300289B |
0.374 |
|
| 2012 |
Toropov AA, Toropova AP, Benfenati E, Gini G, Puzyn T, Leszczynska D, Leszczynski J. Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli. Chemosphere. 89: 1098-102. PMID 22704203 DOI: 10.1016/J.Chemosphere.2012.05.077 |
0.459 |
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| 2012 |
Gajewicz A, Rasulev B, Dinadayalane TC, Urbaszek P, Puzyn T, Leszczynska D, Leszczynski J. Advancing risk assessment of engineered nanomaterials: application of computational approaches. Advanced Drug Delivery Reviews. 64: 1663-93. PMID 22664229 DOI: 10.1016/J.Addr.2012.05.014 |
0.65 |
|
| 2011 |
Puzyn T. On the replacement of empirical parameters in multimedia mass balance models with QSPR data. Journal of Hazardous Materials. 192: 970-7. PMID 21741174 DOI: 10.1016/J.Jhazmat.2011.05.078 |
0.402 |
|
| 2011 |
Puzyn T, Rasulev B, Gajewicz A, Hu X, Dasari TP, Michalkova A, Hwang HM, Toropov A, Leszczynska D, Leszczynski J. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nature Nanotechnology. 6: 175-8. PMID 21317892 DOI: 10.1038/Nnano.2011.10 |
0.692 |
|
| 2011 |
Gajewicz A, Puzyn T, Rasulev B, Leszczynska D, Leszczynski J. Metal oxide nanoparticles: Size-dependence of quantum-mechanical properties Nanoscience and Nanotechnology - Asia. 1: 53-58. DOI: 10.2174/2210681211101010053 |
0.594 |
|
| 2011 |
Puzyn T, Gajewicz A, Rybacka A, Haranczyk M. Global versus local QSPR models for persistent organic pollutants: balancing between predictivity and economy Structural Chemistry. 22: 873-884. DOI: 10.1007/S11224-011-9764-5 |
0.404 |
|
| 2011 |
Puzyn T, Mostrag-Szlichtyng A, Gajewicz A, Skrzyński M, Worth AP. Investigating the influence of data splitting on the predictive ability of QSAR/QSPR models Structural Chemistry. 22: 795-804. DOI: 10.1007/S11224-011-9757-4 |
0.385 |
|
| 2010 |
Mostrag A, Puzyn T, Haranczyk M. Modeling the overall persistence and environmental mobility of sulfur-containing polychlorinated organic compounds. Environmental Science and Pollution Research International. 17: 470-7. PMID 19937279 DOI: 10.1007/S11356-009-0257-7 |
0.332 |
|
| 2010 |
Gajewicz A, Haranczyk M, Puzyn T. Predicting logarithmic values of the subcooled liquid vapor pressure of halogenated persistent organic pollutants with QSPR: How different are chlorinated and brominated congeners? Atmospheric Environment. 44: 1428-1436. DOI: 10.1016/J.Atmosenv.2010.01.041 |
0.315 |
|
| 2009 |
Puzyn T, Leszczynska D, Leszczynski J. Toward the development of "nano-QSARs": advances and challenges. Small (Weinheim An Der Bergstrasse, Germany). 5: 2494-509. PMID 19787675 DOI: 10.1002/Smll.200900179 |
0.463 |
|
| 2009 |
Puzyn T, Mostrag A, Falandysz J, Kholod Y, Leszczynski J. Predicting water solubility of congeners: chloronaphthalenes--a case study. Journal of Hazardous Materials. 170: 1014-22. PMID 19524360 DOI: 10.1016/J.Jhazmat.2009.05.079 |
0.402 |
|
| 2008 |
Puzyn T, Suzuki N, Haranczyk M. How do the partitioning properties of polyhalogenated POPs change when chlorine is replaced with bromine? Environmental Science & Technology. 42: 5189-95. PMID 18754368 DOI: 10.1021/Es8002348 |
0.304 |
|
| 2008 |
Puzyn T, Suzuki N, Haranczyk M, Rak J. Calculation of quantum-mechanical descriptors for QSPR at the DFT level: is it necessary? Journal of Chemical Information and Modeling. 48: 1174-80. PMID 18510372 DOI: 10.1021/Ci800021P |
0.393 |
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| 2008 |
Haranczyk M, Puzyn T, Sadowski P. ConGENER - A tool for modeling of the congeneric sets of environmental pollutants Qsar and Combinatorial Science. 27: 826-833. DOI: 10.1002/Qsar.200710149 |
0.323 |
|
| 2007 |
Puzyn T, Falandysz J. Application and comparison of different chemometric approaches in QSPR modelling of supercooled liquid vapour pressures for chloronaphthalenes. Sar and Qsar in Environmental Research. 18: 299-313. PMID 17514572 DOI: 10.1080/10629360701303875 |
0.347 |
|
| 2007 |
Puzyn T, Falandysz J, Jones PD, Giesy JP. Quantitative structure-activity relationships for the prediction of relative in vitro potencies (REPs) for chloronaphthalenes. Journal of Environmental Science and Health. Part a, Toxic/Hazardous Substances & Environmental Engineering. 42: 573-90. PMID 17454365 DOI: 10.1080/10934520701244326 |
0.347 |
|
| 2007 |
Piliszek S, Wilczyńska-Piliszek AJ, Puzyn T, Falandysz J. Thermodynamical and quantum-chemical characterization and chemometrical selection of representative congeners of trans-chloroazoxybenzene. Journal of Environmental Science and Health. Part a, Toxic/Hazardous Substances & Environmental Engineering. 42: 135-42. PMID 17182383 DOI: 10.1080/10934520601011221 |
0.334 |
|
| 2007 |
Puzyn T, Falandysz J. QSPR modeling of partition coefficients and henry's law constants for 75 chloronaphthalene congeners by means of six chemometric approaches : A comparative study Journal of Physical and Chemical Reference Data. 36: 203-214. DOI: 10.1063/1.2432888 |
0.363 |
|
| 2006 |
Wilczyńska-Piliszek AJ, Puzyn T, Piliszek S, Falandysz J. Selection of representative congener for polychlorinated trans-azobenzenes (PCt-ABs) based on comprehensive thermodynamical and quantum-chemical characterization. Journal of Environmental Science and Health. Part. B, Pesticides, Food Contaminants, and Agricultural Wastes. 41: 1131-42. PMID 16923596 DOI: 10.1080/03601230600856835 |
0.326 |
|
| 2005 |
Puzyn T, Falandysz J. Octanol/water partition coefficients of chloronaphthalenes. Journal of Environmental Science and Health Part a-Toxic\/Hazardous Substances & Environmental Engineering. 40: 1651-1663. PMID 16134358 DOI: 10.1081/Ese-200067976 |
0.318 |
|
| 2004 |
Falandysz J, Puzyn T. 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. 39: 1505-1523. PMID 15244333 DOI: 10.1081/Ese-120037850 |
0.329 |
|
| 2003 |
Puzyn T, Falandysz J. Prediction of log K(OA), T(C), and log P(L) for 281 chlorosubstituted pyrenes as the key parameters featuring environmental transport and fate of these compounds. Journal of Environmental Science and Health. Part a, Toxic/Hazardous Substances & Environmental Engineering. 38: 1761-80. PMID 12940480 DOI: 10.1081/Ese-120022877 |
0.305 |
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