All Competitors

Genomic foundation model training framework whose joint-embedding predictive objective learns functional representations of masked DNA, not tokens.

Multimodal single-cell foundation model whose multiway Transformer jointly models scRNA-seq and scATAC-seq from RNA-only, ATAC-only, or paired inputs.

Retrieval-augmented model for matched molecular pair transformations, proposing localized analog edits guided by retrieved reference compounds.

Transformer that infers whole-genome DNA methylation from gene expression, generalizing zero-shot to unmeasured CpG sites and unseen samples.

Flow-matching generative model for peptide sequence design that learns the protein semantic distribution, fine-tuned for antimicrobial peptides.

SMILES molecular encoder on a DeBERTaV2 backbone, pretrained on 123M PubChem molecules with physicochemical and structural-similarity objectives.

Vision-omics finetuning that aligns pathology foundation models with spatial transcriptomics so morphology features predict local gene expression.

Single-cell foundation model that forecasts how cancer cells evolve, autoregressively generating future gene expression from prior cell states.

Universal all-atom machine-learning force field for molecular dynamics, with ab initio-level accuracy on solvated biomolecules of ~1,500 atoms.

Pan-cancer pretrained diffusion model imputing genome-wide expression from sparse spatial transcriptomics panels, zero-shot and reference-free.

Multimodal generative model predicting viral antigenic change zero-shot from disentangled evolutionary, physicochemical, and structural signals.

Protein-ligand foundation model that maps coarse-grained structural representations directly to binding affinity, running ~26x faster than Boltz-2.

Tokenizer-free genomic foundation model that adaptively chunks raw nucleotides, enabling zero-shot variant fitness and gene essentiality prediction.

Unified drug design engine for protein-ligand structure prediction, binding affinity estimation, and compound generation from Isomorphic Labs.

Vision foundation model for the tree of life, scaling BioCLIP 2 to a ViT-H/14 backbone and more organism images for zero-shot species classification.

Cross-species multimodal foundation model of immunology and inflammation, harmonizing transcriptomics and histology into patient-level embeddings.

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

Masked language model for T-cell receptor and peptide-MHC binding prediction, with compositional pretraining and non-autoregressive decoding.

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

Single-cell foundation model built on masked discrete diffusion, jointly generating gene identities and expression values from 59 million cells.

Joint-embedding predictive foundation model for echocardiography, pretrained on 18M cardiac ultrasound videos for artifact-robust representations.

Vision-language DNA model that renders genomic sequence as visual layouts, reading regions up to 450,000 bases with about 20x better token efficiency.

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

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