All Competitors

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

Autoregressive nucleotide-and-text foundation model generating DNA and RNA sequences from natural-language prompts that name species and function.

Encoder-decoder Transformer that generates intrinsically disordered protein sequences conditioned on target conformational-ensemble descriptors.

Autoregressive language model trained on 37 million intrinsically disordered region sequences, generating IDRs given flanking folded domains.

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

Edit-based flow-matching model that proposes protein variants by learning insertions, deletions, and substitutions on a template sequence.

DNA foundation model using masked discrete diffusion to unify bidirectional sequence understanding and de novo generation in one architecture.

Structure-aware generative DNA language model pretrained on influenza genomes that forecasts future antigenic variants across regions and subtypes.

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

Family of autoregressive genomic foundation models that reconcile k-mer tokenization with single-nucleotide resolution at contexts up to 98k bp.

Metagenomic foundation model trained on 9.7 trillion nucleotide tokens for generative therapeutic design across genes, peptides, and microbiomes.

Diffusion model for de novo AAV capsid design that steers sampling with a viability classifier toward assemblable, packaging-competent variants.

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

Generative antibody model that produces light-chain sequences conditioned on a heavy chain, pairing a RoBERTa encoder with a GPT-2 decoder.

Simplex diffusion model for discrete sequence generation, with released checkpoints for DNA enhancer design and de novo protein sequence design.

Encoder-decoder codon language model that reverse-translates a protein into species-specific coding sequences for synthetic mRNA design.

Long-context generative genomic foundation model with a 98k-nucleotide window, trained on 386 billion bases of eukaryotic DNA for sequence design.

4B-parameter generative genome foundation model trained on assembled environmental metagenomes for microbial representation and de novo DNA design.

Epitope-conditioned T cell receptor generator that writes its own in-context examples, so receptors can be designed for targets with no known binders.

Autoregressive temporal convolutional network for synthetic yeast promoter design, trained with guidance from a sequence-to-expression predictor.

Template-guided protein design model that miniaturizes, diversifies, or expands a natural protein by decoding a fixed-size probabilistic encoding.

Generative biological foundation model placing DNA, RNA, and protein in one shared vocabulary, spanning genomic, proteomic, and cross-molecule tasks.

Homology-aware but alignment-free protein language model using the Mamba state space architecture to condition on hundreds of unaligned homologs.