Qian, Jie published the artcilePredictive and explanatory themes of NOAEL through a systematic comparison of different machine learning methods and descriptors, Safety of n-Octanol, the main research area is machine learning NOAEL food additive cosmetic; Cheminformatics; Food additives; Machine learning; NOAEL; Sub-chronic; Toxicity.
No observed adverse effect level (NOAEL) is an identified dose level which used as a point of departure to infer a safe exposure limit of chems., especially in food additives and cosmetics. Recently, in silico approaches have been employed as effective alternatives to determine the toxicity endpoints of chems. instead of animal experiments Several acceptable models have been reported, yet assessing the risk of repeated-dose toxicity remains inadequate. This study established robust machine learning predictive models for NOAEL at different exposure durations by constructing high-quality datasets and comparing different kinds of mol. representations and algorithms. The features of mol. structures affecting NOAEL were explored using advanced cheminformatics methods, and predictive models also communicated the NOAEL between different species and exposure durations. In addition, a NOAEL prediction tool for chem. risk assessment is provided. We hope this study will help researchers easily screen and evaluate the subacute and sub-chronic toxicity of disparate compounds in the development of food additives in the future.
Food and Chemical Toxicology published new progress about Algorithm. 111-87-5 belongs to class alcohols-buliding-blocks, name is n-Octanol, and the molecular formula is C8H18O, Safety of n-Octanol.
Referemce:
Alcohol – Wikipedia,
Alcohols – Chemistry LibreTexts