| review article | Q7318358 |
| scholarly article | Q13442814 |
| P50 | author | Andrey A Buglak | Q85799336 |
| Anatoly Zherdev | Q58278408 | ||
| P2093 | author name string | Boris B Dzantiev | |
| P2860 | cites work | Metals, toxicity and oxidative stress | Q28250561 |
| Relating Nanoparticle Properties to Biological Outcomes in Exposure Escalation Experiments | Q28384365 | ||
| Quantitative nanostructure-activity relationship modeling | Q28385022 | ||
| Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation | Q28392757 | ||
| Physicochemical properties of nanomaterials: implication in associated toxic manifestations | Q28392831 | ||
| Free radical scavenging and formation by multi-walled carbon nanotubes in cell free conditions and in human bronchial epithelial cells | Q28396543 | ||
| Nanomaterials and nanoparticles: sources and toxicity | Q29615515 | ||
| Optimal descriptor as a translator of eclectic data into endpoint prediction: mutagenicity of fullerene as a mathematical function of conditions. | Q30697213 | ||
| Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticles | Q30722652 | ||
| Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides | Q30851256 | ||
| Summary and analysis of the currently existing literature data on metal-based nanoparticles published for selected aquatic organisms: Applicability for toxicity prediction by (Q)SARs. | Q30993824 | ||
| SiO2 nanoparticles induce cytotoxicity and protein expression alteration in HaCaT cells | Q33533897 | ||
| Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles | Q33859712 | ||
| Cell-specific targeting of nanoparticles by multivalent attachment of small molecules | Q34462201 | ||
| Zinc oxide nanoparticles selectively induce apoptosis in human cancer cells through reactive oxygen species. | Q35787510 | ||
| Applications of Machine Learning Methods in Drug Toxicity Prediction | Q90560616 | ||
| A review on machine learning methods for in silico toxicity prediction | Q90979716 | ||
| Deep learning for predicting toxicity of chemicals: a mini review | Q92032148 | ||
| Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials | Q93008113 | ||
| Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells. | Q50033663 | ||
| In vitro and in vivo genotoxicity tests on fullerene C60 nanoparticles. | Q50053015 | ||
| Nano-QSAR Model for Predicting Cell Viability of Human Embryonic Kidney Cells. | Q50674678 | ||
| QSAR model for predicting cell viability of human embryonic kidney cells exposed to SiO₂ nanoparticles. | Q50798555 | ||
| Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. | Q51413335 | ||
| Machine Learning-Based Modeling of Drug Toxicity. | Q52656757 | ||
| From basic physics to mechanisms of toxicity: the "liquid drop" approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles. | Q54279902 | ||
| Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. | Q55509121 | ||
| A theoretical framework for predicting the oxidative stress potential of oxide nanoparticles | Q63353553 | ||
| Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures | Q64078092 | ||
| Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity | Q72880559 | ||
| Binding of novel fullerene inhibitors to HIV-1 protease: insight through molecular dynamics and molecular mechanics Poisson-Boltzmann surface area calculations | Q85037178 | ||
| Perturbational profiling of nanomaterial biologic activity | Q36677215 | ||
| Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives | Q37215098 | ||
| QSAR modeling of nanomaterials | Q37850776 | ||
| Advancing risk assessment of engineered nanomaterials: application of computational approaches | Q38015999 | ||
| Toxicity of metal oxide nanoparticles: mechanisms, characterization, and avoiding experimental artefacts. | Q38258921 | ||
| Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform | Q38627443 | ||
| Multi-target QSTR modeling for simultaneous prediction of multiple toxicity endpoints of nano-metal oxides | Q38748132 | ||
| Extrapolating between toxicity endpoints of metal oxide nanoparticles: Predicting toxicity to Escherichia coli and human keratinocyte cell line (HaCaT) with Nano-QTTR. | Q38803039 | ||
| Linear and non-linear modelling of the cytotoxicity of TiO2 and ZnO nanoparticles by empirical descriptors. | Q38838799 | ||
| Predicting toxic potencies of metal oxide nanoparticles by means of nano-QSARs. | Q38852385 | ||
| The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method | Q38885692 | ||
| A quasi-QSPR modelling for the photocatalytic decolourization rate constants and cellular viability (CV%) of nanoparticles by CORAL. | Q38917835 | ||
| Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions. | Q38941508 | ||
| Slow release of ions from internalized silver nanoparticles modifies the epidermal growth factor signaling response | Q38953044 | ||
| Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach. | Q38969588 | ||
| Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: hints from nano-QSAR studies | Q38979118 | ||
| Comparing the CORAL and Random Forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials | Q39017173 | ||
| Development of structure-activity relationship for metal oxide nanoparticles | Q39149585 | ||
| Modeling biological activities of nanoparticles | Q39265233 | ||
| Implication of oxidative stress in size-dependent toxicity of silica nanoparticles in kidney cells. | Q39342669 | ||
| Evaluation of genotoxicity of multi-walled carbon nanotubes in a battery of in vitro and in vivo assays | Q39364552 | ||
| Comparative study of predictive computational models for nanoparticle-induced cytotoxicity | Q39691029 | ||
| Studies on the in vitro genotoxicity of baytubes, agglomerates of engineered multi-walled carbon-nanotubes (MWCNT). | Q39896906 | ||
| Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions | Q40320958 | ||
| Quasi-SMILES and nano-QFAR: united model for mutagenicity of fullerene and MWCNT under different conditions. | Q40887481 | ||
| Toxicity of metal oxide nanoparticles in Escherichia coli correlates with conduction band and hydration energies | Q41617871 | ||
| Novel approach for efficient predictions properties of large pool of nanomaterials based on limited set of species: nano-read-across | Q41728796 | ||
| Receptor- and ligand-based study of fullerene analogues: comprehensive computational approach including quantum-chemical, QSAR and molecular docking simulations | Q42280379 | ||
| Size influences the cytotoxicity of poly (lactic-co-glycolic acid) (PLGA) and titanium dioxide (TiO(2)) nanoparticles. | Q42626462 | ||
| Predictive modeling of nanomaterial exposure effects in biological systems. | Q42957668 | ||
| Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles | Q43415971 | ||
| Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach | Q43630055 | ||
| Nano-SAR development for bioactivity of nanoparticles with considerations of decision boundaries | Q44932161 | ||
| Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory. | Q45945538 | ||
| Oxidative stress contributes to silica nanoparticle-induced cytotoxicity in human embryonic kidney cells. | Q46033103 | ||
| In vitro evaluation of cytotoxicity of engineered metal oxide nanoparticles | Q46235956 | ||
| Evaluating the toxicity of TiO2-based nanoparticles to Chinese hamster ovary cells and Escherichia coli: a complementary experimental and computational approach | Q46359093 | ||
| The effect of nitroaromatics' composition on their toxicity in vivo: novel, efficient non-additive 1D QSAR analysis | Q46536667 | ||
| Hierarchic system of QSAR models (1D-4D) on the base of simplex representation of molecular structure | Q46764345 | ||
| Development of Theoretical Descriptors for Cytotoxicity Evaluation of Metallic Nanoparticles. | Q47334876 | ||
| Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli | Q47615270 | ||
| Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins | Q47746759 | ||
| Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. | Q49995324 | ||
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P433 | issue | 24 | |
| P921 | main subject | nanomaterial | Q967847 |
| P577 | publication date | 2019-12-11 | |
| P1433 | published in | Molecules | Q151332 |
| P1476 | title | Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials | |
| P478 | volume | 24 |
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