DynCodon: Multi-objective Dynamic Learning Strategies for Robust Codon Optimization

Existing codon optimization methods often blindly pursue theoretical extreme values for evaluation metrics. However, these extreme sequences at the upper boundary of the Top 10% distribution are like unreplicable "geniuses", lacking universal biological adaptability; in contrast, the robust regions of the Top 10% distribution are more like high-performing "high achievers", containing generalizable evolutionary patterns. This paper proposes a codon optimization model guided by a multi-objective dynamic learning strategy, called DynCodon, aiming to achieve robustness comparable to the expression levels of "high achievers". This model is based on a dual BERT encoder-decoder architecture and employs a two-stage learning paradigm: pre-training through large-scale mixed species to capture universal amino acid-codon correspondences; fine-tuning using this multi-objective dynamic learning strategy for target species to achieve synergistic optimization across multiple key metrics. Experimental results show that DynCodon effectively avoids the pitfall of over-optimization towards extreme values, achieving high-precision reconstruction of natural sequence features within the Top 10% robust regions.