All Competitors

Pan-cancer clinico-genomic model for treatment response and survival prediction, transferring zero-shot to unseen hospitals and cancer types.

Peptide-focused instruction-tuned LLM that describes function, designs sequences, predicts eight bioactivity properties and edits physicochemistry.

Medical imaging vision-language model for chest X-ray, CT and MRI that generates reports, localizes lesions and compares studies over time.

EEG foundation model that decodes by matching neural activity to label text embeddings, with one instruction-tuned checkpoint covering seven tasks.

Vision-language foundation model for coronary angiography that aligns six-view cine studies with procedural reports for zero-shot lesion assessment.

Virtual-cell model that compresses a transcriptome into eight discrete tokens in a reasoning LLM's vocabulary, predicting module-level drug response.

Medical language model compressed to ternary weights, running a 27B-class clinical and biomedical assistant offline from a single 8.48 GB file.

Endoscopy vision-language foundation model pretrained on 348K gastrointestinal examinations that pair routine clinical reports with image sets.

Chemistry embedding model placing SMILES strings and scientific prose in one vector space, top-tier on 43 of 48 molecular and text benchmarks.

Breast-specialized multimodal pathology foundation model for core needle biopsy diagnosis, with conformal risk control gating report release.

Vision-language model that parses Markush structures from patents in a single stage, turning whole-image drawings into machine-readable CXSMILES.

Optical chemical structure recognition model that turns molecule images into SMILES, reaching 93.8% exact match on USPTO with full stereochemistry.

Protein function captioning model fusing sequence, Foldseek structure tokens, and text through a BLIP-2 Q-Former for open-ended free-text annotation.

Mixed-modality metagenomic language model using bidirectional Mamba blocks to embed proteins within 20K tokens of coding and non-coding DNA.

Vision-language model for neuroblastoma pathology that reads H&E slides with their reports to grade tumors, infer biomarkers and stratify risk.

EHR foundation model that reads each ICU hour as clinical text and rolls patient state forward autoregressively in a shared latent space.

Vision-language model that reads molecular structure images, translating them to SMILES, captions, and properties via chemical-bond topology.

Text-guided localization model that grounds natural-language functional descriptions to specific residue regions of a protein sequence.

Single-cell language model that prepends biomedical knowledge-graph tokens to cell sentences, grounding cell type annotation in pathway structure.

Decoder-only foundation model that unifies sequences, 3D structures, and natural language for small molecules and proteins in one shared token space.

Generative scientific foundation model that writes proteins, ligands and their binding interfaces as tokens in one shared grammar, at 1B to 8B scale.

Reasoning LLM that predicts antimicrobial susceptibility of clinical bacterial isolates and supplies mechanistic explanations for each prediction.

Protein-text foundation model placing amino acid sequences and natural language in one token space for protein understanding and de novo design.