The COVID vaccines made one thing obvious: mRNA is a medicine you can design — and the design problem is machine learning. Sample et al. paired ‘polysome profiling of a library of 280,000 randomized 5′ untranslated regions (UTRs) with deep learning,’ then used the model ’to engineer new 5′ UTRs that accurately direct specified levels of ribosome loading’ — extensible to ‘chemically modified RNA … for applications in mRNA therapeutics.’ Karollus et al. added ‘frame pooling, a novel neural network operation,’ to predict ribosome load ‘for 5′UTR of any length,’ and read a beta-thalassemia HBB variant. Wayment-Steele et al. attacked shelf-life: mRNA hydrolysis is beaten by designing structure to lower the ‘average unpaired probability,’ yielding ‘superfolder’ mRNAs with ‘≥two-fold’ half-life. LinearDesign faced ‘around 2.4 × 10^632 candidate mRNA sequences for the SARS-CoV-2 spike protein’ and, reframing it ‘as a lattice parsing problem,’ found an optimum ‘in just 11 minutes,’ raising ‘antibody titre by up to 128 times in mice.’ UTR-LM brought a ‘5′ UTR language model’ whose designs beat a therapeutic baseline by ‘32.5%.’ And Angenent-Mari et al. showed RNA that computes — deep nets predicting toehold-switch function at ‘R2 = 0.43–0.70’ vs ‘0.04–0.15’ for thermodynamic models. Designing the message, base by base.
