All Competitors

Multi-task protein framework recasting function, binding site, and structure prediction as autoregressive next-token prediction over ESM2 embeddings.

Virtual staining models that translate label-free light microscopy into fluorescent-equivalent predictions of nuclei and plasma membranes.

Full-atom peptide binder design against a target pocket, generating backbone frames, side-chain torsions and residue types in one joint flow.

Protein function prediction model that conditions a T5 encoder-decoder on retrieved homologs to assign EC numbers, GO terms and Pfam families.

Neural ab initio reconstruction for cryo-EM and cryo-ET that jointly infers particle poses and a continuous landscape of conformational states.

Discrete diffusion model that generates RNA secondary structure contact maps as pixel-wise segmentation, conditioned on RNA-FM and UFold features.

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.

Generative antibody language model for antigen-binding prediction and de novo paired heavy-light chain design, scaled to 2.4 billion parameters.

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

Homology-aware but alignment-free protein language model using the Mamba state space architecture to condition on hundreds of unaligned homologs.

Molecular property prediction model pretrained by masked reconstruction of SMILES and graph, with disjoint masks that force cross-modal recovery.

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.

Protein conformation sampler that rebuilds AlphaFold2 as a structural diffusion model, generating alternative folded states from sequence alone.

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

Structure-conditioned protein language model aligned to experimental stability data, scoring variant stability and generating stabilized sequences.

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.