In Silico Prediction of Oral Acute Rodent Toxicity Using Consensus Machine Learning

Acute oral toxicity (AOT) is required for the classification
and
labeling of chemicals according to the global harmonized system (GHS).
Acute oral toxicity studies are optimized to minimize the use of animals.
However, with the advent of the three Rs principles and machine learning in toxicology, alternative in silico
methods became a reasonable alternative approach for addressing the
AOT of new chemical matter. Here, we describe the compilation of AOT
data from a commercial database and the development of a consensus
classification model after evaluating different combinations of molecular
representations and machine learning algorithms. The model shows significantly
better performance compared to publicly available AOT models. Its
performance was evaluated on an external validation data set, which
was compiled from the literature, and an applicability domain was
deduced.