All Competitors

Scientific large language model trained on 48 million papers, textbooks, and reference works to store, combine, and reason about scientific knowledge.

3D CT segmentation of abdominal organs and tumors, where a deformable-attention Transformer decodes organ embeddings into the segmentation kernels.

Transformer predicting gene expression from histone modifications, using promoter-enhancer Hi-C interactions to capture distal regulatory effects.

Brain MRI segmentation network with progressive levels of detail, trained across ~160 acquisition sites so one checkpoint handles unseen scanners.

Pathology instance segmentation for glomeruli, nuclei and eosinophils, deforming a bounding circle into a contour rather than a box into an octagon.

Generative transformer pretrained on PubMed abstracts for biomedical text generation and mining, including relation extraction and question answering.

DNA language model for interpretable prediction of 4mC, 5hmC, and 6mA methylation sites across species, using multi-scale k-mer BERT encoders.

Genome-scale language model trained on prokaryotic genes and SARS-CoV-2 genomes to model viral evolution and flag emerging variants of concern.

Protein structure prediction model pairing SE(3)-equivariant networks with a coarse-grained representation to fold sequences fast, without MSA inputs.

Chest CT translation model that synthesizes a virtual expiratory scan from one inspiratory volume, so small airways disease needs no second scan.

Blind protein-ligand docking by diffusion over ligand translations, rotations and torsion angles, reaching 38% top-1 accuracy on PDBBind.

Human-in-the-loop cell segmentation framework enabling custom model training from as few as 100-200 corrected annotations.

Large-scale chemical language model trained on 1.1 billion SMILES strings using linear attention transformers for molecular property prediction.

Word2vec-based language model trained on 360 million microbial genes that predicts gene function from genomic context without sequence homology.

Protein language model reading each residue alongside an unsupervised local-fragment token, so one encoder serves residue- and chain-level tasks.

Predicts CLIP-seq crosslink counts along an RNA sequence base by base, separating protein-specific signal from experimental background.

Self-supervised vision-language model for zero-shot detection of chest X-ray pathologies, trained on image-report pairs without explicit labels.

Medical vision-language pretraining framework that injects structured medical knowledge into radiology image-text learning for VQA and retrieval.

Message passing neural network for fixed-backbone protein sequence design. Achieves 52.4% native sequence recovery, far surpassing Rosetta's 32.9%.

Self-supervised medical vision-and-language pretraining via multi-modal masked autoencoders that reconstruct masked image patches and text tokens.

Virtual staining network that turns label-free multiphoton brain-tissue images into H&E and Perls Prussian Blue histology, from unpaired data.

Protein-nucleic acid complex structure prediction from sequence, folding protein, DNA and RNA chains in one network with confidence estimates.

Pretrained transformer for cell type annotation of scRNA-seq data. Trained on 1.1M cells; outperforms supervised methods on cross-dataset transfer.