Curriculum-Augmented GFlowNets For mRNA Sequence Generation

Designing mRNA sequences is a major challenge in developing next-generation therapeutics, since it involves exploring a vast space of possible nucleotide combinations while optimizing sequence properties like stability, translation efficiency, and protein expression. While Generative Flow Networks are promising for this task, their training is hindered by sparse, long-horizon rewards and multi-objective trade-offs. We propose Curriculum-Augmented GFlowNets (CAGFN), which integrate curriculum learning with multi-objective GFlowNets to generate de novo mRNA sequences. We also provide a new mRNA design environment for GFlowNets which, given a target protein sequence and a combination of biological objectives, allows for the training of models that generate plausible mRNA candidates. This provides a biologically motivated setting for applying and advancing GFlowNets in therapeutic sequence design. On different mRNA design tasks, CAGFN improves Pareto performance and biological plausibility, while maintaining diversity. Moreover, CAGFN reaches higher-quality solutions faster than a GFlowNet trained with random sequence sampling (no curriculum), and enables generalization to out-of-distribution sequences. Imagine a molecule that can be designed to instruct human cells to produce a protein of interest. Such is the promise of messenger RNA (mRNA), which has become a cornerstone of modern biotechnology (Pardi et al., 2018; Sahin et al., 2014). Designing de novo mRNA sequences, that encode a target protein and achieve optimality on particular properties of interest (Gustafsson et al., 2004; Kane, 1995; Mauger et al., 2019), is therefore of growing practical importance. This task can be framed as generating long, structured sequences under multiple, often competing objectives, which makes search and optimization challenging (Keeney & Raiffa, 1993; Zhang et al., 2023; Angermueller et al., 2020). Because biological targets are diverse and downstream outcomes are difficult to predict, diversity is a central design criterion (Mullis et al., 2019). This need is amplified by the limited predictive power of inexpensive screening methods, such as in-silico simulations or in vitro assays.