All Competitors

Structure-aware protein language model pairing amino acid tokens with Foldseek 3Di structural states, outperforming ESM-2 across 10 downstream tasks.

Bilingual protein language model that translates bidirectionally between amino acid sequences and the 3Di structural alphabet for inverse folding.

Protein structure retrieval model aligning 3D structures with functional text via contrastive learning, for zero-shot search of PDB and cryo-EM maps.

Protein representation model adding global fold-similarity and local substructure signals to masked pretraining, reaching 79.2 long-range contact P@L.

Protein sequence-structure encoder biasing attention with relative residue-frame transforms, for inverse folding and peptide binding prediction.

Protein language model pairing sequence with quantized local-structure tokens via disentangled attention, for zero-shot variant effect prediction.

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

Protein structure encoder pretrained by contrastive alignment to a frozen protein language model, anchored by self-supervised contact-map prediction.

Distinguishes experimentally resolved protein structures from predicted ones, pairing a Foldseek 3Di structural language model with a GVP-GNN.

Siamese protein language model whose embedding distances approximate TM-score and lDDT, enabling alignment-free protein structure comparison.

Structure-aware protein language model aligning sequence and 3D structure by contrastive learning, with adapter and LoRA fine-tuning tools.

Protein structure prediction from multiple sequence alignments, trained across MSA depths so one model spans deep alignments and orphan proteins.

Cryo-EM density-map-to-atomic-structure modeling that fuses protein language model embeddings with density voxels, then refines with AlphaFold3.

Protein function captioning model fusing sequence, Foldseek structure tokens, and text through a BLIP-2 Q-Former for open-ended free-text annotation.

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

Protein sequence-structure co-design model conditioned on Gene Ontology function embeddings, sampling residues and backbone angles together.

Multimodal protein language model that adds a continuous-token diffusion head to a discrete pLM, modeling structure without vector quantization.

Protein structure and complex prediction from sequence, in a three-track network that reasons over alignments, distances, and 3D coordinates at once.

Graph deep learning framework fusing frozen protein language model embeddings with structure graphs to predict per-residue flexibility in antibodies.

Ab initio cryo-EM structure modeling that labels density voxels by atom and amino acid type, then threads sequences through them with an HMM.

Partially latent flow-matching model for de novo protein design, jointly generating sequence and all-atom structure for proteins up to 800 residues.

Protein structure embedding model that compresses each 3D fold into a single fixed-length vector for proteome-wide similarity search and clustering.

Multimodal discrete-diffusion protein language model that co-generates amino acid sequence and 3D backbone structure from a single transformer.