{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T05:02:23Z","timestamp":1789794143222,"version":"4.0.1"},"reference-count":111,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["22406080"],"award-info":[{"award-number":["22406080"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]},{"award":["22406080"],"award-info":[{"award-number":["22406080"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2024QB094, ZR2025QC2164Z"],"award-info":[{"award-number":["ZR2024QB094, ZR2025QC2164Z"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Excellent Young Innovation Team of Shandong Higher Education Institutions","award":["2023KJ213"],"award-info":[{"award-number":["2023KJ213"]}]},{"name":"Young Talent of Lifting engineering for Science and Technology in Shandong","award":["SDAST2026QTA083"],"award-info":[{"award-number":["SDAST2026QTA083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>Risk assessment of engineered nanomaterials (ENMs) is essential for protecting human health and the environment. Traditional hazard assessments rely primarily on in vivo testing, which faces technical challenges in extrapolation validity, ethical dilemmas, and high costs. Machine learning (ML) models offer alternative approaches that are aligned with the 3R principles (Replacement, Reduction, and Refinement) for reducing animal use. ML methods help address the economic, ethical, and temporal limitations of traditional nanotoxicology while advancing mechanistic understanding. This review presents a cross-scale framework integrating nano\u2013bio\/nano\u2013environmental interfaces, organ-specific toxicity, in vitro-to-in vivo extrapolation (IVIVE), interpretable ML, and regulatory translation. Future directions include building comprehensive databases to replace sparse literature data, developing ML models that bridge in vitro and in vivo nanotoxicity, incorporating co-exposure scenarios of nanomaterials and chemicals, and further exploring protein\/lipid corona formation and structures.<\/jats:p>","DOI":"10.3390\/molecules31183293","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T08:03:17Z","timestamp":1789632197000},"page":"3293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bridging Nano-Interface Interactions and Organ-Specific Toxicity: A Review of Machine Learning for Nanomaterials Risk Assessment"],"prefix":"10.3390","volume":"31","author":[{"given":"Wei","family":"Li","sequence":"first","affiliation":[{"name":"School of Chemistry and Chemical Engineering, Ludong University, Yantai 264025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanfang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Statistics, Shandong Technology and Business University, Yantai 264005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianqin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Chemistry and Chemical Engineering, Ludong University, Yantai 264025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiana","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Chemistry and Chemical Engineering, Ludong University, Yantai 264025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7394-0955","authenticated-orcid":false,"given":"Yang","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Chemistry and Chemical Engineering, Ludong University, Yantai 264025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajun","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Chemistry and Chemical Engineering, Ludong University, Yantai 264025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12641","DOI":"10.1021\/acsnano.7b07093","article-title":"Predicting Nano\u2013Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling","volume":"11","author":"Wang","year":"2017","journal-title":"ACS Nano"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8575","DOI":"10.1021\/acs.chemrev.3c00070","article-title":"Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation","volume":"123","author":"Yan","year":"2023","journal-title":"Chem. 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