All Competitors

Unified 100-billion-parameter protein language model combining autoencoding and autoregressive objectives for protein understanding and generation.

De novo protein backbone diffusion model reaching 92% designability without structure-prediction pretraining, sampling in 100 steps on one GPU.

Protein complex structure prediction system combining AlphaFold2 and AlphaFold3 with stoichiometry prediction, MSA engineering, and model ranking.

All-atom protein diffusion model that co-designs backbone, sequence and sidechains by denoising a superposition over all 20 sidechain states.

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

Inverse protein folding model for all-atom structures with bound ligands, nucleotides, or metal ions. Reaches 75.7% sequence recovery at metal sites.

Zero-shot protein conformation sampling that perturbs an input structure and anneals it back with a score network trained only on crystal structures.

RNA 3D structure prediction pipeline pairing a transformer (RNAformer) that predicts inter-nucleotide geometries with Rosetta energy minimization.

Mixture-of-experts protein language model scaling to 16 billion parameters, applied to variant effect prediction and de novo protein design.

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

Single-sequence protein structure predictor that adapts image diffusion to generate 2D inter-residue templates, folding proteins without an MSA.

Protein representation learning from cryo-EM density maps, transferring to flexibility, active-site, binding-affinity, and stability tasks.

MSA-free all-atom protein structure prediction, folding monomers from the representations of a 3B-parameter metagenomic protein language model.

RNA 3D structure reconstruction from cryo-EM density maps, using a 3D U-Net that predicts 18 atom types and assigns sequence by global alignment.

Protein language model that supervises embedding geometry with inter-residue contacts, so representation distance tracks physical distance.

MSA-free protein structure prediction that replaces multiple sequence alignments with a protein language model pre-trained on billions of sequences.

All-atom structure prediction for complexes of proteins, DNA, RNA, and small molecules, using Min-SNR diffusion weighting and the Muon optimizer.

Protein language model that captures short- and long-range residue co-evolution through a dual pre-training objective, at 3B parameters.

Protein structure prediction and peptide binder design model covering the 20 canonical amino acids plus 29 noncanonical residues.

Protein stability predictor scoring ΔΔG for substitutions, multi-point mutations and indels from a folding model's latent structure representations.

Backmapping model that rebuilds all-atom protein and nucleic acid structures from coarse-grained beads and inpaints unresolved residues.

Open-source Apache-2.0 reproduction of AlphaFold3 that predicts all-atom structures of proteins, RNA, DNA, small molecules, and their complexes.

Structure-free virtual screening model co-embedding protein residues and small molecules from sequence and 2D chemistry, scoring a compound in 10 ms.