Metal-oxide nanoparticles find widespread applications in mundane life today, and cost-
effective evaluation of their cytotoxicity and ecotoxicity is essential for sustainable progress …

Metal-oxide nanoparticles find widespread applications in mundane life today, and cost-
effective evaluation of their cytotoxicity and ecotoxicity is essential for sustainable progress …

Safety concerns of engineered nanoparticles (ENPs) hamper their applications and
commercialization in many potential fields. Machine learning has been proved as a great …

Computational modeling, particularly with machine learning models, has been of significant
interest for non-animal testing of nanotoxicity. Machine learning algorithms find a …

Organic chemistry has seen colossal progress due to machine learning (ML). However, the
translation of artificial intelligence (AI) into materials science is challenging, where biological …

Metal oxide nanoparticles (MeOxNPs) production is expected to increase every year
exponentially, and their potential to cause adverse effect to the environment and human …

Abstract Development of nanotoxicity prediction models is becoming increasingly important
in the risk assessment of engineered nanomaterials. However, it has significant obstacles …

A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs),
including 15 physicochemical, structural and assay-related descriptors, was enriched with …

The rapid advance of nanotechnology has led to the development and widespread
application of nanomaterials, raising concerns regarding their potential adverse effects on …

Developing predictive modeling frameworks of potential cytotoxicity of engineered
nanoparticles is critical for environmental and health risk analysis. The complexity and the …