All Competitors

Pretrained antibody structure predictor that outputs full paired heavy/light 3D structures faster than protein language models generate embeddings.

Biohub's 2026 protein language model trained on ~2.8 billion sequences, forming the representation core of its world model of protein biology.

Biohub's structure-prediction and design engine that turns ESMC sequence representations into atomic-resolution 3D structures of proteins and biomolecular complexes.

An RNA language model trained by self-supervised masked-nucleotide prediction on ~50,000 IRES sequences that predicts secondary-structure features rivaling experimental chemical probing.

A single-step generative model for protein structure prediction and binder design that reaches AlphaFold3-level accuracy with a claimed ~15x inference speedup.

SE(3)-invariant masked autoencoder pretrained on ~370K AlphaFold-DB structures for protein fold representation learning, enabling frozen-feature and zero-shot fold classification.

A ligand-conditioned masked discrete diffusion model that co-designs protein sequence and structure under explicit small-molecule constraints.

A unified bio-language Mixture-of-Experts foundation model spanning DNA, protein sequence and structure, and biological text, applied across eight task families from a single checkpoint.

A reasoning-guided foundation model for de novo antibody CDR design, pairing a multimodal-LLM understanding expert with a Boltz-1-based diffusion generation expert.

Generative multimodal foundation model that jointly models DNA, RNA, protein, and cellular context across six biological modalities, with SOTA splicing prediction.

A generative protein-protein docking model that adapts AlphaFold-Multimer via flow matching, replacing the template module with a docking module.

Generative pipeline for epitope-targeted de novo antibody (nanobody) CDR design that yields nanomolar binders from only dozens of designs per antigen.

Enhanced 464M-parameter version of Protenix with substantial gains in antibody-antigen complex prediction over v1, plus target-conditioned VHH-Fc generative design with up to 48% hit rates.

A 730M-parameter protein foundation model that co-designs enzyme sequence and 3D structure under small-molecule ligand guidance for de novo enzyme design.

Transformer-based protein language diffusion model generating all-atom intrinsically disordered protein conformational ensembles, validated against experimental NMR and SAXS data.

All-atom E(3)-equivariant diffusion model that refines RNA structures by resolving steric clashes and completing missing atoms.

Physics-informed generative model that pairs flow matching with a Mamba state-space backbone for linear-time protein backbone design, scaling to 2,000+ residues.

A single-nucleotide-resolution RNA foundation model pretrained on non-coding RNAs with ELECTRA-style replaced-token detection for RNA regulatory inference.

Geometric deep learning model that learns universal atomic-scale representations of intermolecular interfaces across proteins, small molecules, ions, lipids, and nucleic acids.

A sequence-to-ensemble predictor that generates experiment-consistent conformational ensembles of intrinsically disordered proteins by pairing deep-learning fragment prediction with physics-aware assembly.

Protein-ligand foundation model that maps coarse-grained structural representations directly to binding affinity, running ~26x faster than Boltz-2.

Isomorphic Labs' unified AI drug design engine that doubles AlphaFold 3 accuracy on protein-ligand structure prediction and approaches gold-standard FEP+ methods for binding affinity.

A global protein structure tokenizer whose successive tokens add increasing detail, enabling adaptive-length representations, better generation, and zero-shot protein design.

A distilled deep learning model that predicts structural similarity between proteins directly from sequence, reaching up to 258x speedups for large-scale homology search.