1c:I[776865,["/_next/static/chunks/bb923814e1f4d2e9.js","/_next/static/chunks/dd3ee9f5b7716ca6.js","/_next/static/chunks/4204a0ae82542ca8.js","/_next/static/chunks/1270dead247da766.js","/_next/static/chunks/6e1aa89a642f40be.js","/_next/static/chunks/2d7aa753e81324a8.js","/_next/static/chunks/8ffd0b71567920d8.js","/_next/static/chunks/67ea6dfd21f16c5f.js","/_next/static/chunks/24eb4379f68013fd.js","/_next/static/chunks/b8cd775ffdf935bd.js","/_next/static/chunks/983103cd9eb7b428.js","/_next/static/chunks/263f2cdcb9aa2341.js","/_next/static/chunks/294b9525248c8e5f.js"],"Grid"] 1d:I[617291,["/_next/static/chunks/bb923814e1f4d2e9.js","/_next/static/chunks/dd3ee9f5b7716ca6.js","/_next/static/chunks/4204a0ae82542ca8.js","/_next/static/chunks/1270dead247da766.js","/_next/static/chunks/6e1aa89a642f40be.js","/_next/static/chunks/2d7aa753e81324a8.js","/_next/static/chunks/8ffd0b71567920d8.js","/_next/static/chunks/67ea6dfd21f16c5f.js","/_next/static/chunks/24eb4379f68013fd.js","/_next/static/chunks/b8cd775ffdf935bd.js","/_next/static/chunks/983103cd9eb7b428.js","/_next/static/chunks/263f2cdcb9aa2341.js","/_next/static/chunks/294b9525248c8e5f.js"],"GridCol"] 1e:I[734616,["/_next/static/chunks/bb923814e1f4d2e9.js","/_next/static/chunks/dd3ee9f5b7716ca6.js","/_next/static/chunks/4204a0ae82542ca8.js","/_next/static/chunks/1270dead247da766.js","/_next/static/chunks/6e1aa89a642f40be.js","/_next/static/chunks/2d7aa753e81324a8.js","/_next/static/chunks/8ffd0b71567920d8.js","/_next/static/chunks/67ea6dfd21f16c5f.js","/_next/static/chunks/24eb4379f68013fd.js","/_next/static/chunks/b8cd775ffdf935bd.js","/_next/static/chunks/983103cd9eb7b428.js","/_next/static/chunks/263f2cdcb9aa2341.js","/_next/static/chunks/294b9525248c8e5f.js"],"Avatar"] 1f:I[432972,["/_next/static/chunks/bb923814e1f4d2e9.js","/_next/static/chunks/dd3ee9f5b7716ca6.js","/_next/static/chunks/4204a0ae82542ca8.js","/_next/static/chunks/1270dead247da766.js","/_next/static/chunks/6e1aa89a642f40be.js","/_next/static/chunks/2d7aa753e81324a8.js","/_next/static/chunks/8ffd0b71567920d8.js","/_next/static/chunks/67ea6dfd21f16c5f.js","/_next/static/chunks/24eb4379f68013fd.js","/_next/static/chunks/b8cd775ffdf935bd.js","/_next/static/chunks/983103cd9eb7b428.js","/_next/static/chunks/263f2cdcb9aa2341.js","/_next/static/chunks/294b9525248c8e5f.js"],"default"] 20:T4d2,We present the facile synthesis of a clickable polymer library with systematic variations in length, binary composition, pK_a, and hydrophobicity (clog P) to optimize intracellular pDNA and CRISPR-Cas9 ribonucleoprotein (RNP) performance. We couple physicochemical characterization and machine learning to interpret quantitative structure-property relationships within the combinatorial design space. For the first time, we reveal unexpected disparate design parameters for nucleic acid carriers; via explainable machine learning on 432 formulations, we discover that lower polymer pK_a and higher percentages of benzimidazole ethanethiol enhance pDNA delivery, yet polymer length and captamine cation identity improve RNP delivery. Closed-loop Bayesian optimization of 552 formulation ratios further enhances in vitro performance. The top three polymers yield a higher signal and stable transgene expression over 20 days in vivo, and a 1.7-fold enhancement over controls. Our facile coupling of synthesis, characterization, and machine analysis provides powerful tools to quantitate performance parameters accelerating next-generation vehicles for nucleic acid medicines.21:T7f2,

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