All Competitors

Sequence-only TM-score prediction pairing frozen ProtT5 embeddings with a bidirectional GRU and multi-scale convolution for protein homology search.

Multi-modal protein foundation model aligning 3D structure and literature text to a sequence anchor through contrastive pretraining.

Structure-based protein encoder that voxelizes every heavy atom into a 3D grid, learning orientation-robust representations for protein function.

Deep network that predicts structures of full biological assemblies: proteins, nucleic acids, small molecules, metals, and covalent modifications.

Multi-modal protein language model using the MSA evolutionary profile as a reasoning step between structure and sequence. 650M outperforms ESM-3 1.4B.

All-atom protein generation model that samples side chains, backbone, and sequence together from a single diffusion process over atom coordinates.

Protein backbone generation from voxelized 3D densities, pairing a 3D CNN autoencoder with latent flow matching for shape and motif conditioning.

Structure-aware protein language model using structure-guided masking and a causal objective for variant effect prediction and protein discovery.

Protein language model that tokenizes sequence, backbone structure, and text into one vocabulary for function prediction, design, and fold editing.

Protein complex structure assembly guided by predicted inter-chain domain-domain distances, averaging TM-score 0.769 across 46 CASP13-15 targets.

Flow-matching protein backbone generator with hierarchical fold-class conditioning, reaching 99.0% designability and chains up to 800 residues.

Protein structure prediction from a single sequence, with no multiple sequence alignment. Folds a 384-residue protein in 14.2 seconds on one GPU.

Graph transformer over 3D protein structures predicting solvation free energy, hydrodynamic radius, diffusion constants, and molecular volume.

Protein structure prediction model pairing SE(3)-equivariant networks with a coarse-grained representation to fold sequences fast, without MSA inputs.

RNA-protein complex refinement via diffusion, repositioning the protein against the RNA to improve AlphaFold 3 and ProRNA3D-single backbones.

Protein structure tokenizer that encodes a whole structure globally, with each successive token adding detail for adaptive-length representations.

Protein structure tokenizer that encodes all-atom folds as artificial amino acids and decodes them back to coordinates at TM-score above 0.96.

Open-source reproduction of AlphaFold 3 that predicts structures of proteins, DNA, RNA, and small-molecule ligands, including their mixed complexes.

Structure-based protein-RNA screening that reads position-wise nucleotide preferences off a single complex structure and ranks libraries in seconds.

Whole-protein carbohydrate-binding classification from structure, for proteome-scale screening with graph convolutions over ESM2 embeddings.

End-to-end RNA 3D structure prediction from sequence alone, coupling the RNA-FM language model with an Invariant Point Attention structure module.

Co-generative protein language model decoding sequence and structure tokens together from GO functional annotations for de novo protein design.

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