All Competitors

Autoregressive generative model that uses reinforcement learning to optimize mRNA codon sequences for MFE, CAI, and GC content.

Reinforcement-learning generative framework for multi-objective RNA codon optimization that generalizes across six species and five RNA types.

Foundation model that predicts microRNA-mRNA target specificity from sequence, using a dual-encoder trained with a symmetric contrastive objective.

Codon-level mRNA language model adapted from ESM-2 650M by swapping amino-acid tokens for codon tokens, transferring protein knowledge to mRNA tasks.

Hybrid framework that predicts ribosome location profiles from mRNA sequence alone, pairing a structure-aware TASEP simulation with a Mamba polisher.

Autoregressive model for therapeutic mRNA design that jointly generates 5' UTR, CDS, and 3' UTR, pretrained on 30 million full-length natural mRNAs.

Generative RNA foundation model trained on 114 million full-length sequences for de novo design of tRNAs, aptamers, CRISPR guide RNAs, and mRNAs.

Multimodal reverse-translation language model that generates species-aware mRNA coding sequences from protein sequences, conditioned on host taxonomy.

Transformer that generates multi-species antibody and nanobody framework regions at the mRNA level, conditioned on input CDRs, across six species.

Coding-sequence foundation model for mRNA design, pretrained as a BART denoising encoder-decoder on mRNA from nine taxonomic groups.

mRNA language foundation model trained on ~115M protein-coding sequences across the tree of life, unifying mRNA perception and generation.

GPT-style generative language model for mRNA coding sequences, pretrained across bacteria, eukaryotes, and archaea for de novo CDS design.

Transformer language model for 5' UTR sequences that predicts mRNA translation efficiency, ribosome loading, and protein expression levels.