All Competitors

AlphaFold fine-tuned on peptide-MHC and protein-peptide binding data for specificity prediction across MHC class I/II, PDZ, and SH3 domains.

Cyclic peptide structure prediction and de novo macrocycle design, by wrapping a frozen structure predictor's positional encoding into a ring.

Protein language model that annotates intrinsically disordered regions per residue from sequence alone, without MSAs or biophysical features.

Invariant point attention network that segments a protein structure into domains, clustering residues bottom-up on experimental and AlphaFold models.

Medical vision-language pretraining unifying fusion-encoder and dual-encoder designs, handling image-only, text-only, and paired inputs in one model.

Histopathology semantic masks generated de novo from noise, then rendered as photorealistic H&E or PD-L1 patches by a paired image-translation GAN.

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 800+ species with explicit species conditioning, separating conserved regulatory motifs from background bias.

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

MHC class II peptide presentation model that scores epitopes over AlphaFold2-derived residue contact graphs and rates anti-drug antibody risk.

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.

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

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

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.