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Due to the recurrent and difficult\u2010to\u2010treat nature of UC symptoms, current pharmacological treatments fail to meet patients' expectations. This study presents a machine learning\u2010assisted high\u2010throughput screening strategy to expedite the discovery of efficient nanozymes for UC treatment. Therapeutic requirements, including antioxidant property, acid stability, and zeta potential, are quantified and predicted by using a machine learning model. Non\u2010quantifiable attributes, including intestinal barrier repair efficacy and biosafety, are assessed via high\u2010throughput screening. Feature significance analysis, sure independence screening, and sparsifying operator symbolic regression reveal the high\u2010dimensional structure\u2010activity relationships between material features and therapeutic needs. SrDy\n                    <jats:sub>2<\/jats:sub>\n                    O\n                    <jats:sub>4<\/jats:sub>\n                    with high stability, low toxicity, targeting ability, and reactive oxygen species (ROS) scavenging capability is identified, which reduces ROS production, lowers cytochrome C levels in cytoplasm, and inhibits apoptosis in intestinal epithelial cells by stabilizing the mitochondrial membrane potential. Mice treated with SrDy\n                    <jats:sub>2<\/jats:sub>\n                    O\n                    <jats:sub>4<\/jats:sub>\n                    show improvements in colon length and body weight compared with dextran sodium sulfate salt\u2010treated model group. Transcriptomic and 16S rRNA sequencing analyses show that SrDy\n                    <jats:sub>2<\/jats:sub>\n                    O\n                    <jats:sub>4<\/jats:sub>\n                    boosts beneficial gut bacteria, and decreases pathogenic bacteria, thereby effectively restoring gut microbiota balance. Moreover, SrDy\n                    <jats:sub>2<\/jats:sub>\n                    O\n                    <jats:sub>4<\/jats:sub>\n                    offers the advantage of X\u2010ray imaging without side effects.\n                  <\/jats:p>","DOI":"10.1002\/adma.202417536","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T02:59:13Z","timestamp":1736737153000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Machine Learning\u2010Assisted High\u2010Throughput Screening of Nanozymes for Ulcerative Colitis"],"prefix":"10.1002","volume":"37","author":[{"given":"Xianguang","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Digestive Diseases Huashan Hospital Fudan University  12 Middle Urumqi Road Shanghai 200040 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixin","family":"Yu","sequence":"additional","affiliation":[{"name":"College 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