All Competitors

Tri-modal protein language model aligning sequence, structure, and text in one embedding space for natural-language search over billions of proteins.

Multimodal protein language model extending ESM-2 and SaProt with a Structure Adapter over residue torsion angles for protein function prediction.

EEG foundation model that learns transferable brain-signal representations with a vector-quantized tokenizer and masked transformer pretraining.

Multi-modal foundation model for sleep analysis, learning joint representations across brain, cardiac, and respiratory polysomnography signals.

ECG foundation model that learns 12-lead waveform representations by contrastively aligning each recording with machine-generated cardiological text.

Keypoint-based foundation model for brain MRI registration, pretrained on over 100,000 3D volumes for rigid, affine, and deformable alignment.

Whole-slide histopathology foundation model pretrained on 1.3 billion image tiles from 171,189 clinical slides spanning 31 tissue types.

Vision-language foundation model pre-trained on screening mammogram-report pairs to improve data efficiency and robustness in breast cancer detection.

Ophthalmic imaging foundation model pretrained on 2.78M images across 11 modalities for diagnosis, prognosis, and visual question answering.

Self-supervised 3D cell segmentation for fluorescence microscopy, pairing WNet3D with Swin-UNetR to segment volumes without annotated training data.

Universal brain lesion segmentation for multi-modal brain MRI, using a Mixture of Modality Experts to span diverse modalities and lesion types.

Efficient protein language model library from Prescient Design enabling high-quality sequence representations and fitness prediction in 24 GPU hours.

Mixture-of-Experts foundation model for medical image segmentation that generalizes across imaging modalities and clinical centers.

Trainable, open-source reimplementation of AlphaFold2 for protein structure prediction that matches its accuracy and runs 3-5x faster.

Deep learning framework predicting equilibrium distributions of molecular systems, enabling efficient ensemble generation and conformation sampling.

Compact 22M-parameter ViT-S pathology foundation model pre-trained on 195M tiles, spanning subcellular segmentation to slide-level prediction.

Diffusion-based structure prediction model for biomolecular complexes, spanning proteins with DNA, RNA, small molecules, ions, and modified residues.

Discrete generative model for antibody protein sequences combining MCMC walks on a smoothed energy landscape with one-step denoising jumps.

CLIP-style vision-language model for echocardiography, pretrained on echo videos and cardiologist reports for zero-shot cardiac interpretation.

Single-cell foundation model that treats cells as tokens and tissues as sentences, encoding cell-cell relationships across 85M parameters.

Conversational agent that answers plain-language questions about DNA, RNA, and protein sequences by coupling a genomics encoder to a language model.

Family of medical multimodal models built on Gemini, adding uncertainty-guided web search, custom modality encoders, and long-context EHR reasoning.

Interpretable model of human transcription initiation that decomposes promoter activity into a minimal set of sequence rules at base-pair resolution.

Generalist medical vision-language foundation model with 40B parameters, spanning radiology, pathology, dermatology, retinography, and endoscopy.