All Competitors

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

A deep generative decoder that infers microRNA expression directly from bulk or single-cell mRNA gene expression via a shared mRNA/miRNA latent space.

A masked discrete-diffusion model over millions of full-length mRNAs, guided by Monte Carlo Tree Search for joint codon optimization and de novo UTR design.

An RNA language model trained by self-supervised masked-nucleotide prediction on ~50,000 IRES sequences that predicts secondary-structure features rivaling experimental chemical probing.

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 conditional encoder-decoder language model that designs RNA sequences under simultaneous secondary-structure, fixed-base, and coding constraints.

Generative multimodal foundation model that jointly models DNA, RNA, protein, and cellular context across six biological modalities, with SOTA splicing prediction.

An autoregressive nucleotide-and-text foundation model pretrained on ~416B characters from 2,221 eukaryotic species for natural-language-guided conditional generation of DNA and RNA sequences.

A 110M-parameter multimodal RNA language model that designs RNA sequences from secondary structure, consensus, and Gene Ontology constraints via discrete diffusion.

Long-context (64 kb) RNA foundation model using the Striped-Hyena architecture for zero-shot prediction of transcriptome architecture from unspliced pre-mRNA sequence.

Modular deep-learning framework for 3D-structure-based RNA sequence design, pairing a direct GNN predictor (SCRU-Seq) and a diffusion model (SCRU-Diff) built on self-contained RNA units.

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.

A transformer that predicts protein-RNA binding affinity from Boltz-2 pre-structural embeddings via cross-modal attention, without requiring predicted structures.

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.

All-atom E(3)-equivariant diffusion model that refines RNA structures by resolving steric clashes and completing missing atoms.

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

A single-nucleotide-resolution RNA foundation model pretrained on non-coding RNAs with ELECTRA-style replaced-token detection for RNA regulatory inference.

Geometric deep learning model that learns universal atomic-scale representations of intermolecular interfaces across proteins, small molecules, ions, lipids, and nucleic acids.

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.

Cross-species, multimodal foundation model of immunology and inflammation that harmonizes transcriptomics and histology into unified patient-level representations.

A structure-aware graph-attention model that predicts A-to-I RNA editing across tissues and species from sequence and secondary structure, with released pretrained weights.

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