用课程学习提升生成模型，高效设计高稳定高表达的mRNA序列
Curriculum-Augmented GFlowNets For mRNA Sequence Generation
- 分阶段逐步增加序列长度，引导模型从简单到复杂任务学习
- 在多目标优化下生成更符合生物特性的mRNA，且多样性不下降
- 适合药物研发人员和生成模型研究者快速探索新序列设计
设计mRNA序列是下一代治疗药物开发的关键挑战，需在海量核苷酸组合中优化稳定性、翻译效率和蛋白表达。尽管生成流网络（GFlowNets）在此任务上具有潜力，但其训练受限于稀疏的长程奖励和多目标权衡。本文提出课程增强型GFlowNets（CAGFN），将课程学习与多目标GFlowNets结合，通过基于长度的课程策略，逐步引导模型从较短序列向更长序列探索。我们还构建了一个新的mRNA设计环境，可针对特定蛋白序列和多种生物学目标训练生成模型，提供生物合理的应用框架。在多个mRNA设计任务中，CAGFN在帕累托性能、生物合理性方面均优于基线，且收敛更快；相比随机采样训练的GFlowNet，CAGFN能更快找到高质量解，并具备对分布外序列的泛化能力。
原文摘要 · Abstract (English)
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. CAGFN integrates a length-based curriculum that progressively adapts the maximum sequence length guiding exploration from easier to harder subproblems. 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.
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