All Competitors

De novo protein backbone generation by SE(3) diffusion over rigid-body frames. Samples designable monomers up to 500 residues without pretraining.

Geometric relational graph neural network that encodes 3D protein structures through geometry-aware message passing and self-supervised pretraining.

Masked DNA language model trained on over 800 vertebrate genomes and conditioned on species identity to learn conserved regulatory sequence features.

Masked DNA language model trained on 800+ species with explicit species conditioning, separating conserved regulatory motifs from background bias.

Antibody language model pretrained on 18M OAS Fv sequences with a 70% CDR-H3-targeted mask, giving embeddings for pairing and binding prediction.

Protein structure accuracy estimation predicting a global TM-score from an equivariant graph network over residue geometry and Rosetta energy terms.

Parameter-efficient protein language model that matches larger models such as ESM-2 on protein prediction tasks using under 10% of the parameters.

Structure prediction for protein, RNA, and protein-RNA complexes in one AlphaFold2-derived framework that accepts MSA or language model encoders.

DNA foundation models from 500M to 2.5B parameters, trained on 3,200+ human genomes and 850 species for variant effect prediction.

In silico saturation mutagenesis in a single forward pass, scoring every substitution in a 2 kb window across 2,002 chromatin profiles.

Self-supervised pretraining framework for medical imaging that unifies pixel restoration with contrastive learning across 2D and 3D image backbones.

Antibody CDR design model that reprograms a frozen English BERT for sequence infilling, avoiding training a dedicated protein language model.

Multi-modal protein language model trained on sequences paired with biomedical text, enabling zero-shot function prediction and text-based retrieval.

Transformer framework for single-cell multi-omics that predicts cross-modality relationships using heterogeneous graphs of cells, genes, and proteins.

RNA language model pre-trained on 2M+ pre-mRNA sequences from 72 vertebrate species for splice-site prediction and variant effect analysis.

Gene expression prediction from histone modifications, combining self-attention with dense convolutions and transfer learning across cell types.

Text-conditioned latent diffusion model that generates synthetic chest X-rays from free-form radiology prompts by adapting Stable Diffusion.

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.