All Competitors

Flow cytometry foundation model that reads heterogeneous antibody panels through a universal marker embedding to predict sample-level phenotypes.

Neuro-symbolic inverse folding that turns a backbone into a Potts model and hands it to an automated-reasoning solver for constrained design.

Anti-phage defense gene predictor fusing protein language model embeddings with a contrastive genomic-context transformer over 64-gene windows.

Autoregressive model for therapeutic mRNA design that jointly generates 5' UTR, CDS, and 3' UTR, pretrained on 30 million full-length natural mRNAs.

Gastrointestinal histopathology foundation model pretrained on 353 million multi-scale patches from 210,000 H&E whole-slide images of GI tissue.

Discrete diffusion model that designs regulatory DNA with tunable cell-type-specific activity and learns activity-predictive representations.

Single-cell foundation model combining regulatory network priors with a Mamba backbone for clustering, batch integration, and perturbation modeling.

Protein foundation model for de novo enzyme design that co-designs sequence and 3D structure under small-molecule ligand guidance, at 730M parameters.

Structural brain MRI foundation model pretrained on synthetic healthy volumes only, giving frozen features for brain age, dementia risk and image QC.

Generative imaging model simulating single-cell fluorescence microscopy for all 12,800 human proteins in the Human Protein Atlas.

Generates single-cell transcriptomes from structured biological metadata via contrastive language-omics pretraining and a diffusion transformer.

Compact 167M-parameter protein language model built on a multiplicative LSTM, giving zero-shot variant effect and fitness prediction from sequence.

EEG foundation model pretrained by spectrogram reconstruction that improves online directional motor-imagery brain-computer interface control.

Virtual cell model using masked discrete diffusion over the whole transcriptome to simulate scRNA-seq perturbation responses across tissues.

Protein-language diffusion model generating all-atom conformational ensembles for intrinsically disordered proteins and disordered regions.

Transformer that predicts protein-RNA binding affinity from Boltz-2 pre-structural embeddings via cross-modal attention, with no 3D structure step.

Joint embedding predictive architecture for at-home polysomnography, encoding full-night seven-channel recordings for sleep staging and disease risk.

Generative RNA foundation model trained on 114 million full-length sequences for de novo design of tRNAs, aptamers, CRISPR guide RNAs, and mRNAs.

Molecular foundation models pretrained on density functional theory data, encoding 3D geometry and quantum behavior for ADMET and drug discovery.

Autoregressive generative model for protein molecular dynamics that emits flexible-length trajectories frame by frame with anti-drifting sampling.

Multimodal reasoning LLM for protein function prediction, fusing protein language model embeddings to emit interpretable GO-term reasoning traces.

Protein function prediction model that autoregressively generates Gene Ontology terms from amino acid sequence instead of classifying fixed labels.

Histopathology foundation model with 1.1B parameters, trained entirely on public data using JEDI, a dual-stage strategy combining JEPA and DINO.