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Polymer\u2010based delivery systems are particularly promising due to their versatile chemical structure and convenient adaptability, but struggle with the toxicity\u2010efficiency dilemma. Introducing anionic, hydrophilic, or \u201cstealth\u201d functionalities represents a promising approach to overcome this dilemma in gene delivery. Here, two sets of diblock terpolymers are created comprising hydrophobic poly(<jats:italic>n<\/jats:italic>\u2010butyl acrylate) (P<jats:italic>n<\/jats:italic>BA), a copolymer segment made of hydrophilic 4\u2010acryloylmorpholine (NAM), and either the cationic 3\u2010guanidinopropyl acrylamide (GPAm) or the 2\u2010carboxyethyl acrylamide (CEAm), which is negatively charged at neutral conditions. These oppositely charged sets of diblocks are co\u2010assembled in different ratios to form mixed micelles. Since this experimental design enables countless mixing possibilities, a machine learning approach is applied to identify an optimal GPAm\/CEAm ratio for achieving high transfection efficiency and cell viability with little resource expenses. After two runs, an optimal ratio to overcome the toxicity\u2010efficiency dilemma is identified. The results highlight the remarkable potential of integrating machine learning into polymer chemistry to effectively tackle the enormous number of conceivable combinations for identifying novel and powerful gene transporters.<\/jats:p>","DOI":"10.1002\/smll.202306116","type":"journal-article","created":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T19:17:42Z","timestamp":1696533462000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Optimization of Mixed Micelles Based on Oppositely Charged Block Copolymers by Machine Learning for Application in Gene Delivery"],"prefix":"10.1002","volume":"20","author":[{"given":"Katharina","family":"Leer","sequence":"first","affiliation":[{"name":"Laboratory of Organic and Macromolecular Chemistry Friedrich Schiller University Jena  Humboldtstrasse 10 07743 Jena Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li\u00ean 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