All Competitors

Specificity Foundation Model that predicts microRNA-mRNA target specificity from sequence using a physics-derived dual-encoder with symmetric contrastive learning.

A cross-modal transfer-learning model that adapts the ESM-2 650M protein language model to mRNA analysis by swapping amino-acid tokens for codon tokens, applied to mRNA benchmarks without re-training.

A hybrid simulation and machine-learning framework that predicts ribosome location profiles from mRNA sequence alone, combining a structure-aware TASEP with a Mamba polisher.

Autoregressive generative model pretrained on 30 million full-length natural mRNA sequences that jointly optimizes 5' UTR, CDS, and 3' UTR for therapeutic mRNA stability and translation efficiency.

Long-context generative RNA foundation model trained on 114 million full-length RNA sequences, supporting de novo design of tRNAs, aptamers, CRISPR guide RNAs, mRNAs, and circular RNAs.

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.

A BART-based foundation model for mRNA coding-sequence analysis, pretrained by denoising across nine taxonomic groups and fine-tunable for expression, stability, and riboswitch tasks.

An mRNA language foundation model trained on ~115M coding sequences across the tree of life for unified mRNA sequence perception and generation.

GPT-2-style generative foundation model pretrained on ~165M mRNA coding sequences across all three domains of life for de novo CDS generation and design.

A transformer language model pretrained on 5' UTR sequences across five species to predict mRNA translation efficiency, ribosome loading, and expression levels.