2021
DOI: 10.1101/2021.02.22.432301
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

NanoTox: Development of a parsimonious in silico model for toxicity assessment of metal-oxide nanoparticles using physicochemical features

Abstract: Metal-oxide nanoparticles find widespread applications in mundane life today, and cost-effective evaluation of their cytotoxicity and ecotoxicity is essential for sustainable progress. Machine learning models use existing experimental data, and learn the relationship of various features to nanoparticle cytotoxicity to generate predictive models. In this work, we adopted a principled approach to this problem by formulating a feature space based on intrinsic and extrinsic physico-chemical properties, but exclusi… Show more

Help me understand this report
View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2022
2022
2022
2022

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 60 publications
(41 reference statements)
0
1
0
Order By: Relevance
“…Nontoxic predictors were the number of oxygen atoms (NOxygen), surface area (SurfArea), conduction band energy (Ec), and core size (CoreSize). 231 The NanoTox platform is an opensource, freely available platform under the GNU General Public License that enables access to nanotoxicology reports.…”
Section: Methods For Nanotoxicity Assessmentmentioning
confidence: 99%
“…Nontoxic predictors were the number of oxygen atoms (NOxygen), surface area (SurfArea), conduction band energy (Ec), and core size (CoreSize). 231 The NanoTox platform is an opensource, freely available platform under the GNU General Public License that enables access to nanotoxicology reports.…”
Section: Methods For Nanotoxicity Assessmentmentioning
confidence: 99%