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This study established an interpretable machine learning (ML) framework to identify and predict GenX\u2010induced non\u2010monotonic dose\u2013response (NMDR) relationships in zebrafish (\n                    <jats:styled-content style=\"fixed-case\">\n                      <jats:italic>Danio rerio<\/jats:italic>\n                    <\/jats:styled-content>\n                    ). By integrating 263 independent experimental records across 11 biological categories, we benchmarked 6\u2009mL paradigms, with the CatBoost\u2010based Master Model demonstrating exceptional predictive fidelity (AUC\u2009=\u20090.977; Accuracy\u2009=\u200991.25%). Mechanistic interpretation via SHAP (SHapley Additive exPlanations) analysis revealed that \u201cEffect Measurement\u201d and \u201cBiological Category\u201d are the primary determinants of hormetic responses. Specifically, the immune, endocrine, and reproductive systems, alongside neurobehavioral endpoints, exhibited the highest sensitivity to low\u2010dose stimulation. Structure\u2013activity relationship (SAR) analysis identified lipophilicity and surface distribution descriptors (\n                    <jats:italic>MolLogP, SlogP_VSA3)<\/jats:italic>\n                    , electrostatic state and surface area descriptors (\n                    <jats:italic>VSA_EState<\/jats:italic>\n                    series), and topological connectivity index (\n                    <jats:italic>Chi1v<\/jats:italic>\n                    ) as core structural drivers of hormesis. These findings suggest that GenX\u2010induced hormesis stems from a strategic metabolic trade\u2010off, where optimized electronic distribution and lipid\u2010mediated transport trigger adaptive overcompensation via signaling pathways like oxidative stress. This research provides a quantitative tool and a robust scientific basis for the ecological risk assessment of emerging PFAS alternatives, shifting the paradigm from empirical data fitting to mechanistic predictive generalization.\n                  <\/jats:p>","DOI":"10.1002\/iub.70135","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T04:19:57Z","timestamp":1790828397000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Hormesis Effects of\n                    <scp>GenX<\/scp>\n                    in Zebrafish via Interpretable Machine Learning: Insights From\n                    <scp>SHAP<\/scp>\n                    Analysis"],"prefix":"10.1002","volume":"78","author":[{"given":"Yanwei","family":"Xu","sequence":"first","affiliation":[{"name":"SEP Key Laboratory of Eco\u2010Industry, School of Resources and Civil Engineering Northeastern University  Shenyang People's 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