Use of metal/metal oxide spherical cluster and hydroxyl metal coordination complex for descriptor calculation in development of nanoparticle cytotoxicity classification model$
暂无分享,去创建一个
Kwang Youn Kim | K. No | K T No | H. Shin | J. W. Park | H K Shin | K Y Kim | J W Park
[1] W. Tong,et al. Molecular dynamics simulations and applications in computational toxicology and nanotoxicology. , 2017, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.
[2] A. Nel,et al. Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles. , 2011, Small.
[3] Jerzy Leszczynski,et al. Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach. , 2014, Ecotoxicology and environmental safety.
[4] Rong Liu,et al. Nano-SAR development for bioactivity of nanoparticles with considerations of decision boundaries. , 2013, Small.
[5] Jerzy Leszczynski,et al. Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides , 2014, Environmental Science and Pollution Research.
[6] Lutz Mädler,et al. Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation. , 2012, ACS nano.
[7] Robert Rallo,et al. Use of a high-throughput screening approach coupled with in vivo zebrafish embryo screening to develop hazard ranking for engineered nanomaterials. , 2011, ACS nano.
[8] Bernd Nowack,et al. Analysis of the occupational, consumer and environmental exposure to engineered nanomaterials used in 10 technology sectors , 2012, Nanotoxicology.
[9] Jerzy Leszczynski,et al. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. , 2011, Nature nanotechnology.
[10] Jerzy Leszczynski,et al. Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: Hints from nano-QSAR studies , 2015, Nanotoxicology.
[11] Steven J Enoch,et al. Adverse Outcome Pathway (AOP) Informed Modeling of Aquatic Toxicology: QSARs, Read-Across, and Interspecies Verification of Modes of Action. , 2016, Environmental science & technology.
[12] Jerzy Leszczynski,et al. Zeta Potential for Metal Oxide Nanoparticles: A Predictive Model Developed by a Nano-Quantitative Structure–Property Relationship Approach , 2015 .
[13] H. Ohtaki,et al. Structure and dynamics of hydrated ions , 1993 .
[14] H. Kahler,et al. The Crystalline Structures of Sputtered and Evaporated Metallic Films. , 1921 .
[15] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[16] Peter Moeck,et al. Crystallography Open Database (COD): an open-access collection of crystal structures and platform for world-wide collaboration , 2011, Nucleic Acids Res..
[17] Lang Tran,et al. There's plenty of room at the forum: Potential risks and safety assessment of engineered nanomaterials , 2007 .
[18] Jerzy Leszczynski,et al. Advancing risk assessment of engineered nanomaterials: application of computational approaches. , 2012, Advanced drug delivery reviews.
[19] Andrew P Worth,et al. A theoretical framework for predicting the oxidative stress potential of oxide nanoparticles , 2011, Nanotoxicology.
[20] Andrey A Toropov,et al. Optimal descriptor as a translator of eclectic information into the prediction of membrane damage by means of various TiO(2) nanoparticles. , 2013, Chemosphere.
[21] James J. P. Stewart,et al. Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters , 2012, Journal of Molecular Modeling.
[22] Edward R. Dougherty,et al. Is cross-validation valid for small-sample microarray classification? , 2004, Bioinform..
[23] F. Stellacci,et al. A general mechanism for intracellular toxicity of metal-containing nanoparticles† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr01234h Click here for additional data file. , 2014, Nanoscale.
[24] M. Hormozi-Nezhad,et al. Using nano-QSAR to determine the most responsible factor(s) in gold nanoparticle exocytosis , 2015 .
[25] Ingmar Persson,et al. Hydrated metal ions in aqueous solution: How regular are their structures? , 2010 .
[26] R. Parr,et al. Hardness, softness, and the fukui function in the electronic theory of metals and catalysis. , 1985, Proceedings of the National Academy of Sciences of the United States of America.
[27] J. Spreadborough,et al. High-temperature X-ray diffractometer , 1959 .
[28] Roberto Todeschini,et al. Molecular descriptors for chemoinformatics , 2009 .
[29] M T D Cronin,et al. A conceptual framework for predicting the toxicity of reactive chemicals: modeling soft electrophilicity , 2006, SAR and QSAR in environmental research.
[30] T. Xia,et al. Development of structure-activity relationship for metal oxide nanoparticles. , 2013, Nanoscale.
[31] Nina Nikolova-Jeliazkova,et al. QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review , 2005, Alternatives to laboratory animals : ATLA.
[32] E. Roduner. Size matters: why nanomaterials are different. , 2006, Chemical Society reviews.
[33] R. Todeschini,et al. Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing / Volume II: Appendices, References , 2009 .
[34] Gerta Rücker,et al. y-Randomization and Its Variants in QSPR/QSAR , 2007, J. Chem. Inf. Model..