| scholarly article | Q13442814 |
| P50 | author | Alexander Tropsha | Q4720252 |
| Denis Fourches | Q29460345 | ||
| Qingxin Mu | Q56452726 | ||
| P2093 | author name string | Hongyu Zhou | |
| Liwen Li | |||
| Bing Yan | |||
| Dongqiuye Pu | |||
| Gaoxing Su | |||
| P2860 | cites work | Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests | Q22673963 |
| Understanding biophysicochemical interactions at the nano-bio interface | Q23909863 | ||
| Oxidative stress and dermal toxicity of iron oxide nanoparticles in vitro | Q23910151 | ||
| Chemical basis of interactions between engineered nanoparticles and biological systems | Q27021154 | ||
| Toxic potential of materials at the nanolevel | Q28295314 | ||
| Repeated administrations of carbon nanotubes in male mice cause reversible testis damage without affecting fertility | Q28383162 | ||
| Quantitative nanostructure-activity relationship modeling | Q28385022 | ||
| Functionalized carbon nanotubes for potential medicinal applications | Q28392143 | ||
| Best Practices for QSAR Model Development, Validation, and Exploitation | Q28649930 | ||
| Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research | Q28748220 | ||
| Safe handling of nanotechnology | Q29617531 | ||
| Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity | Q31152707 | ||
| Predictive QSAR modeling workflow, model applicability domains, and virtual screening | Q33316534 | ||
| A nano-combinatorial library strategy for the discovery of nanotubes with reduced protein-binding, cytotoxicity, and immune response | Q33320667 | ||
| Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method | Q33535332 | ||
| Reducing nanotube cytotoxicity using a nano-combinatorial library approach | Q33565859 | ||
| Functionalized carbon nanotubes specifically bind to alpha-chymotrypsin's catalytic site and regulate its enzymatic function | Q33622411 | ||
| Dependence of QSAR models on the selection of trial descriptor sets: a demonstration using nanotoxicity endpoints of decorated nanotubes. | Q34517977 | ||
| Recent advances in graphene family materials toxicity investigations | Q36454755 | ||
| Protein binding modulates the cellular uptake of silver nanoparticles into human cells: implications for in vitro to in vivo extrapolations? | Q37172031 | ||
| Weighted feature significance: a simple, interpretable model of compound toxicity based on the statistical enrichment of structural features | Q37420417 | ||
| Toward the development of "nano-QSARs": advances and challenges | Q37605014 | ||
| Exploring quantitative nanostructure-activity relationships (QNAR) modeling as a tool for predicting biological effects of manufactured nanoparticles | Q37833114 | ||
| Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential. | Q38060983 | ||
| Perturbation of physiological systems by nanoparticles. | Q38197488 | ||
| Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species | Q38508018 | ||
| 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 | ||
| QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells | Q39169538 | ||
| Modeling biological activities of nanoparticles | Q39265233 | ||
| Inhalation toxicity studies: OECD guidelines in relation to REACH and scientific developments | Q40095732 | ||
| Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis | Q42646937 | ||
| Evidence-based toxicology - the toolbox of validation for the 21st century? | Q42757209 | ||
| Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles | Q43415971 | ||
| Nanotoxicology. Nanotechnology grows up. | Q44942898 | ||
| Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery | Q45787855 | ||
| Charge density effects on the aggregation properties of poly(p-phenylene-ethynylene)-based anionic polyelectrolytes | Q46728165 | ||
| Nanotechnology, energy and markets. | Q50603842 | ||
| Nanoparticle dispersity in toxicology. | Q51531075 | ||
| Novel variable selection quantitative structure--property relationship approach based on the k-nearest-neighbor principle | Q52082245 | ||
| ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors | Q54376731 | ||
| Nano-quantitative structure–activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells | Q57008918 | ||
| Nanotechnologies, engineered nanomaterials and occupational health and safety – A review | Q57251973 | ||
| Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data | Q57529102 | ||
| Construction of coherent nano quantitative structure–properties relationships (nano-QSPR) models and catastrophe theory | Q62511151 | ||
| Predicting toxicity of nanoparticles | Q63353556 | ||
| Nano-combinatorial chemistry strategy for nanotechnology research | Q82780792 | ||
| Nanomaterial cell interactions: how do carbon nanotubes affect cell physiology? | Q83021923 | ||
| Nano-combinatorial and catalyst screening technologies | Q83310032 | ||
| P433 | issue | 3 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | computer-aided design | Q184793 |
| carbon nanotube | Q1778729 | ||
| P304 | page(s) | 374-83 | |
| P577 | publication date | 2016-01-01 | |
| P1433 | published in | Nanotoxicology | Q1964708 |
| P1476 | title | Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles | |
| P478 | volume | 10 |
| Q38787849 | Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches |
| Q36379062 | Elucidation of the Molecular Determinants for Optimal Perfluorooctanesulfonate Adsorption Using a Combinatorial Nanoparticle Library Approach. |
| Q92632878 | Practices and Trends of Machine Learning Application in Nanotoxicology |
| Q47337295 | Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling |
| Q94477921 | QSAR without borders |
| Q50057829 | Reproducibility, sharing and progress in nanomaterial databases. |
| Q38875385 | Toward a systematic exploration of nano-bio interactions |
| Q31106801 | Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation |
| Q91118452 | Universal nanohydrophobicity predictions using virtual nanoparticle library |
Search more.