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

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 …

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

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

Establishing toxicological predictive modeling frameworks for heterogeneous nanomaterials
is crucial for rapid environmental and health risk assessment. However, existing structure …

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

Application of predictive modeling approaches can solve the problem of missing data.
Numerous studies have investigated the effects of missing values on qualitative or …

Computational modeling has earned significant interest as an alternative to animal testing of
toxicity assessment. However, the process of selecting an appropriate algorithm and fine …

The application of in silico methods in the risk assessment of metal oxide nanoparticles
(MNPs) and data gap filling has found profound usability. Followed by the success of …

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