PolluCarc-MFSE: A Multi-Fingerprint Stacking Ensemble Learning Model for Chemical Pollutant Carcinogenicity

Traditional carcinogenicity assessment relies on animal experiments, which are costly, time-consuming, and difficult to extrapolate across species. Consequently, the carcinogenic potential of most chemical pollutants remains uncharacterized, posing significant risks to human health. Here, we combined 2356 compounds from public databases with six molecular fingerprints, five base machine learning models, and ensemble strategies to develop PolluCarc-MFSE (a multifingerprint stacking ensemble learning model using Klekota-Roth, extended-connectivity fingerprint (ECFP), and MACCS (Molecular ACCess System) fingerprints) as the optimal predictive model. This model was then applied to predict the carcinogenicity of 179 petrochemical pollutants with unknown carcinogenic potential, as listed in the emission standard of pollutants for the petroleum chemistry industry (GB 31571-2015, 2024 amendment). Among 213 carcinogenic compounds (130 predicted by the model and 83 identified via dataset intersection), the major types were high-ring-number polycyclic aromatic hydrocarbons, halogenated unsaturated hydrocarbons, and multisubstituted aromatics. To assess biological relevance, disease-associated targets for ten pollutant-linked cancers were retrieved from GeneCards. Using a novel multimetric scoring approach in network analysis, we identified 29 hub targets (e.g., JAK2, EGFR, TP53, MYC) that exhibit stable binding to the predicted carcinogens. Pathway enrichment confirmed involvement in cancer-related, PI3K-Akt, and JAK-STAT signaling pathways, while Disease Ontology linked these targets to liver, breast, and lung malignancies. This integrated "data-model-mechanism" framework provides a practical tool for prioritizing high-risk chemical pollutants and demonstrates how computational toxicology can support regulatory decision-making and the adoption of New Approach Methodologies in chemical risk assessment.