Computational insights into exploring the potential effects of environmental contaminants on human health.

ABSTRACT: With rapid industrialization and urbanization, the increasing prevalence of air and water pollutants poses a significant threat to public health. Traditional research methods, such as epidemiological studies and in vitro/in vivo experiments, provide valuable biological insights but are often costly, time-consuming, and limited in scale. To address this gap, this study develops a machine learning-based approach to predict the carcinogenicity of pollutants. Using the dataset of carcinogenic and non-carcinogenic molecules that we collected, the pretrained KPGT model trained with molecular fingerprints and descriptors achieved an AUC of 0.83, surpassing traditional machine learning models. To validate this model, common pollutants from air and water sources were analyzed. Further clustering cl

SUBMITTER: Cao F 

PROVIDER: S-EPMC11973197 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature