All Competitors

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

Paired-sequence protein language model that jointly encodes two interacting chains 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 predicting antibody-antigen binding specificity from sequence with a dual-encoder, cross-attentive architecture.

BERT-style language model for somatic mutations, pretrained on cancer sequencing from 210,000+ patients for tumor subtyping and therapy response.

Genomic language models fine-tuned to detect and classify antibiotic resistance genes, catching divergent ARGs that reference alignment misses.

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

Masked DNA language model for regulatory genomics with a motif-discovery regularizer for zero-shot TF motif recovery and variant effect prediction.

Connects a frozen protein language model to a general LLM via a cross-modal projector, adding protein reasoning without catastrophic forgetting.

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

Causal 309M-parameter protein language model that scores variant fitness zero-shot and generates sequences, reaching 0.390 Spearman on ProteinGym.

LoRA adapter on ProstT5 predicting per-residue distributions over Foldseek 3Di tokens, capturing conformational flexibility from MD trajectories.

Single-cell RNA-seq language model that treats cells as gene-expression tokens, synthesizing whole transcriptomes from tissue and disease metadata.

Antibody developability predictor pairing text and protein language models, using in-context learning to fit new assays without retraining.

Masked DNA language model with a learnable, adaptive tokenizer that produces context-dependent, variable-length segments instead of fixed k-mers.

Gut microbiome foundation model pretrained on human shotgun metagenomes, learning species-level taxonomic representations for disease prediction.

Generative language model for phenotype-driven drug discovery, proposing small-molecule structures from up- and down-regulated gene signatures.

Peptide language model trained on HELM notation, a DeBERTa encoder for property prediction on macrocyclic and non-canonical medium-sized peptides.

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

Protein-family language model trained on unaligned homolog sets for zero-shot variant fitness prediction and design. ProFam-1 holds 251M parameters.

Multimodal protein language model that adds a continuous-token diffusion head to a discrete pLM, modeling structure without vector quantization.

Pan-viral genomic language model producing fixed genome-level embeddings of viral DNA and RNA, reused across classification tasks without retraining.

Conversational single-cell and spatial multi-omics brain foundation model, with zero-shot cell annotation and disease prediction across species.

Single-cell foundation model adapting LLaMA-3.1-8B with LoRA, recasting transcriptomes and protein interaction networks as natural-language Q&A pairs.