All Competitors

Self-supervised single-cell foundation model that predicts masked gene embeddings in latent space using a joint-embedding predictive architecture.

Retrieval-augmented inverse folding model that fuses structural motif retrieval with a hybrid attention decoder to design sequences for a backbone.

Retrieval-augmented latent diffusion model for protein binder design, retrieving interfaces in a shared latent space across peptides and antibodies.

Protein structure autoencoder compressing backbone coordinates into a latent space, paired with a latent diffusion model for generative design.

Autoregressive protein language model for antibody Fc domains, reinforcement-tuned to design variants with programmable Fc-receptor binding profiles.

Medical vision-language model that takes visual prompts on an image and returns answers grounded in pixel-level segmentation masks.

Protein foundation model with 3B parameters, pretrained jointly on sequence and 3D structure via masked language modeling and diffusion denoising.

Diffusion model over coarse-grained protein topology that samples diverse folds, handing them to RFdiffusion to build de novo protein backbones.

De novo ligand design framework that generates protein binders and small molecules by inverting gradients through a differentiable docking model.

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

Compact protein fitness predictor that fuses within-family evolutionary profiles with inverse-folding logits for zero-shot variant effect prediction.

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

Protein language model conditioned on ensembles of computed conformations, giving state-aware embeddings for interaction, localization, and function.

All-atom protein representation model that learns from each residue's strictly local atomic neighborhood, capturing side-chain geometry and chemistry.

Structure-conditioned fine-tune of ESM2 for protein mutation-effect prediction, matching ESM3-level accuracy after roughly an hour of fine-tuning.

GPCR structure prediction and peptide design model that generates linear and cyclic peptide agonists carrying noncanonical amino acids, zero-shot.

De novo peptide sequencing model that aligns tandem mass spectra with protein language model embeddings through constrained optimization.

Flow-based protein structure tokenizer, a diffusion autoencoder replacing SE(3)-invariant components with global coordinates and standard attention.

Distilled few-step protein backbone generator that adapts Score Identity Distillation to Proteina for over 20x faster de novo structure sampling.

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.

Geometric deep learning scoring function for protein-ligand binding affinity, pretrained on synthetic complexes and fine-tuned on PDBbind structures.

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.