Machine Learning for Designing Next-Generation mRNA Therapeutics

sequences with precisely defined translation efficiencies. We emphasize recent developments in design algorithms that rely on activation maximization and generative modeling to improve both the fitness and diversity of designed sequences. Compared with prior approaches such as genetic algorithms, we show that these approaches are not only faster but also less likely to get stuck in local sequence optima. Finally, we discuss how the approach reviewed here can be generalized to other gene regions and applications.