All Competitors

A reasoning LLM fine-tuned on clinical antimicrobial-susceptibility data augmented with mechanistic rationales, predicting susceptibility with explanations for novel isolate-antibiotic pairs.

A protein-text foundation model embedding sequences and natural language in a shared token space, enabling protein understanding and de novo design from one checkpoint.

A hyperbolic protein language model for alignment-free phylogenetic inference, producing distance matrices for tree placement without multiple sequence alignment.

A 700M-parameter DNA language model pretrained on the rice pangenome, built as a reusable foundation model for crop genomics and molecular breeding.

Contrastive promoter-protein pretraining that aligns bacterial promoters with their encoded proteins to learn regulatory genomics representations.

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 promptable DNA language model that generates multi-kilobase plasmid sequences from human-readable component specifications, post-trained with verifiable rewards.

A latent diffusion model with ControlNet-style conditioning for post-translational-modification-aware protein sequence design.

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

A pretrained DNA language model combining Mamba state-space layers, gated dilated convolutions, and Fourier-based attention to capture multi-scale genomic regulatory patterns.

A co-generative protein language model that jointly decodes sequence and structure tokens from GO functional annotations for de novo functional protein design.

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

A Qwen3-4B language model that reads and reasons over single cells by tokenizing scRNA-seq with residual vector quantization and training with verifiable reinforcement learning.

A Llama 3 model fine-tuned on a ChEMBL corpus that designs molecular linkers from natural-language geometry and physicochemical prompts, without task-specific re-training.

A compact 167M-parameter sequence-only protein language model using a multiplicative LSTM that achieves competitive ProteinGym fitness prediction without structure or attention.

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 paired-sequence protein language model that jointly encodes interacting proteins to predict interactions, binding affinity, and interface contacts.

Hierarchical language model for atlas-level cell-type annotation of scATAC-seq data that annotates new query datasets without retraining.

Contrastive antibody language model that predicts antibody-antigen binding specificity directly from amino acid sequence using a dual-encoder, cross-attentive architecture.

BERT-style language model that learns contextual representations of somatic mutations from clinical sequencing of >210,000 patients for cancer subtyping and therapy response.

A family of genomic language models fine-tuned from a pretrained DNA language model to detect and classify antibiotic resistance genes beyond reference-database limits.

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