All Competitors

Multi-modal, multi-task biological foundation model trained on 2 billion samples spanning proteins, small molecules, and single-cell gene expression.

Single-cell model that ranks the genes driving a cell state transition, using a gene graph-enhanced manifold pretrained on 20 million cells.

Open foundation model for photoplethysmography (PPG), learning morphology-aware waveform representations for cardiovascular and wearable health tasks.

Self-supervised vision transformer pretrained on chest X-rays to produce a domain-specific foundation model for classification and lung segmentation.

Pocket-conditioned peptide designer: twin diffusion models generate an inhibitor backbone from receptor pocket geometry, then predict its sequence.

Text-guided molecule generation by linking a pretrained scientific text encoder to a frozen molecular language model with a cross-attention adapter.

Chemical perturbation model generating post-treatment transcriptomes for compounds and cell lines never screened, from SMILES structure and dose.

Genomic language model that labels adapter sequences in nanopore direct-RNA reads base by base, then splits the chimeric reads those adapters create.

Molecular foundation model that late-fuses graph, image, and SMILES encoders into one embedding for molecular property and drug target prediction.

De novo peptide generation from four ProtGPT2 fine-tunes, one per design goal: hemolytic, non-hemolytic, non-fouling, and soluble sequences.

Protein conformational ensemble generator that fine-tunes ESM3 with masked discrete diffusion over structure tokens instead of 3D coordinates.

All-atom structure tokenizer that turns proteins, RNA and small molecules into discrete 3D tokens and decodes them back below 1 Å RMSE.

Region-aware bilingual medical multimodal LLM that handles image- and region-level vision-language tasks across eight imaging modalities.

Spot detection and quantification in 5D fluorescence microscopy. Pretrained 2D and 3D U-Nets segment foci, then Gaussian fitting measures each one.

Protein complex interface quality assessment model that pairs persistent homology barcodes with a graph attention network to predict DockQ scores.

Protein inverse folding by categorical diffusion, tuned with reinforcement learning on structural consistency. Samples diverse refoldable sequences.

Genomic sequence classification answered through natural-language prompts by one GPT-2 pretrained on mixed DNA and English under one BPE vocabulary.

Diffusion model that jointly generates a 3D molecule and its shape, electrostatic surface and pharmacophores for ligand-based drug design.

Refines the CDR loops of a predicted antibody structure with SE(3) flow matching, steered at sampling time by bond, angle and torsion potentials.

De novo peptide sequencing model that retrieves a similar peptide-spectrum match from a database and fuses it into transformer decoding.

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

Molecular docking and design foundation model that unifies structure-based drug design and peptide design at the atom level in one checkpoint.

Multimodal protein function annotation that scores a sequence against free-text descriptions, including GO and EC labels unseen during training.