All Competitors

Graph diffusion transformer for in-context molecular design, adapting to new tasks from a few molecule-property demonstrations without fine-tuning.

Tabular foundation model adapted for extreme feature counts, enabling in-context prediction on wide omics tables with tens of thousands of features.

SE(3)-equivariant flow-matching model for pocket-aware 3D ligand generation, predicting binding affinity and confidence in the same network.

Single-cell foundation model with a Hyena backbone that translates across omics layers, predicting protein abundance from transcriptomes zero-shot.

Kidney-specialized single-cell foundation model trained across four mammalian species for zero-shot cell-type annotation and batch integration.

Brain MRI foundation model pairing DenseNet and Vision Transformer backbones with mixture of experts for disease diagnosis and brain age prediction.

Predicts single-cell scRNA-seq coverage and scATAC-seq insertion profiles from DNA sequence, adapting the Borzoi trunk with a cell-specific decoder.

Multimodal scientific foundation model unifying protein, DNA/RNA, and small-molecule structure in one token vocabulary for cross-domain reasoning.

Neuro-oncology foundation model for brain tumor MRI, using distributionally robust pretraining for molecular subtyping and survival prediction.

Self-supervised foundation model for 3D brain MRI, learning transferable anatomical representations from unlabeled scans for disease classification.

Modality-agnostic foundation model for human brain imaging that runs five core neuroimaging tasks across uncalibrated CT and MRI without retraining.

Biological reasoning language model that post-trains Qwen3 with reinforcement learning from verifiable rewards for drug-discovery tasks.

Single-cell foundation model for yeast that injects regulatory network priors into transformer attention for zero-shot and fine-tuned analysis.

Reasoning language model post-trained on virtual cell simulations, answering questions about gene perturbations and their effects in natural language.

Multimodal foundation model predicting genome-wide binding of chromatin-associated proteins from protein sequence, DNA sequence, and chromatin state.

Self-supervised bioacoustic audio encoder that turns animal-sound recordings into transferable embeddings for species classification and detection.

Histology vision transformer with 80M parameters that predicts spatial gene expression from H&E tissue images and transfers to tumor detection.

RNA foundation model pretrained jointly on sequences and secondary structures for structure prediction, homology and splice site classification.

Multimodal, retrieval-augmented protein foundation model that learns family-specific evolutionary constraints with optional structure conditioning.

Proteome-scale protein language model whose representations enable zero-shot protein-protein interaction and gene essentiality prediction.

Single-cell transcriptomics foundation model that encodes gene regulatory network structure into self-attention through graph signal processing.

Zebrafish single-cell foundation model built on the Geneformer framework, producing frozen gene and cell embeddings for developmental analysis.

Open medical multimodal models from Google, built on Gemma 3 with a medically tuned SigLIP vision encoder for clinical text and image understanding.

Medically tuned SigLIP encoder from Google that maps medical images and text into one embedding space for zero-shot classification and retrieval.