Prediction of Respiratory Irritation and Respiratory Sensitization of Chemicals Using Structural Alerts and Machine Learning Modeling.

ABSTRACT: Inhalation of toxic substances and contaminants can have adverse effects on the respiratory tract, leading to a range of health problems, such as irritation and inflammation, allergic reaction and asthma, lung damage, or even death. It is not possible to experimentally evaluate respiratory toxicity for all the thousands of chemicals in use. Here, we generated structural alerts and developed machine learning (ML) classification models to predict respiratory irritation and respiratory sensitization hazards of chemicals using experimental data from publicly available databases and the literature. We identified 13 structural alerts for respiratory irritants and 18 structural alerts for respiratory sensitizers. We also developed a set of models for each hazard using different types of molecular

SUBMITTER: Chushak Y 

PROVIDER: S-EPMC12030883 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature