All Competitors

Open-source structure prediction model for proteins, nucleic acids, and small molecules, trained on public data to AlphaFold3-level accuracy.

Generative model that emulates protein equilibrium ensembles, sampling cryptic pockets and unfolded states far faster than molecular dynamics.

Diffusion model for 3D small-molecule design against protein-protein interaction sites, guided by the natural binding peptide or protein partner.

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

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

Per-residue AlphaFold2 pLDDT confidence regressed from sequence by a bidirectional LSTM, with no structure prediction and no database lookup.

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

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

Protein backbone generation with a diffusion model whose noise schedule is derived from the renormalization group rather than heuristically tuned.

AMPLIFY post-trained to absorb 3D structure from protein graph networks, lifting trRosetta contact precision from 0.253 to 0.320 at P@L/5.

Evolution-guided diffusion model that generates temporal protein folding pathways, from unfolded chain to native state, rather than static structures.

De novo transmembrane protein design by joint all-heavy-atom sequence and structure diffusion, validated by a 1.7 A crystal structure.

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

464M-parameter structure prediction and design model that improves antibody-antigen complex accuracy over Protenix-v1 and adds generative VHH design.

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

Ternary complex structure predictor for PROTACs and molecular glues, placing E3 ligase, degrader, and target protein in one SE(3)-equivariant pass.

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

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

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

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

Generative multimodal foundation model spanning DNA, RNA, and protein, with any-to-any inference across genome, transcriptome, and proteome.

ESM-2 post-trained to absorb 3D structure from protein graph networks, raising contact precision on a withheld CASP16 set by 59%.

Protein-ligand cofolding model that predicts 3D complex structures with SO(3)-equivariant diffusion, trained on physics-based synthetic data.