All Competitors

A generative protein-dynamics model that predicts short-horizon MD trajectories using rectified flow matching in velocity space over residue frames and torsions.

Diffusion-based backbone generation and sequence design method for programmable asymmetric transmembrane beta-barrel nanopores.

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

Specificity Foundation Model that predicts enzyme-substrate specificity from sequence using a physics-derived dual-encoder with symmetric contrastive learning.

Specificity Foundation Model that predicts peptide-MHC binding specificity from sequence using a physics-derived dual-encoder with symmetric contrastive learning.

Transformer framework that models protein-protein interactions at residue resolution, generalizing zero-shot to unseen MHC alleles and sequence-neutral PTMs from one fixed checkpoint.

A hierarchical three-stage cascade that generates chemically modified and macrocyclic peptides in HELM notation, supporting de novo design and constrained infilling.

Protein pretraining framework that learns representations directly from cryo-EM density maps, transferring to flexibility, active-site, binding-affinity, and stability tasks.

Channel-adaptive autoregressive generative model that synthesizes in-silico fluorescence microscopy of protein subcellular localization from amino-acid sequence and cellular landmark stains.

A protein-text foundation model embedding sequences and natural language in a shared token space, enabling protein understanding and de novo design from one checkpoint.

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.

A hyperbolic protein language model for alignment-free phylogenetic inference, producing distance matrices for tree placement without multiple sequence alignment.

Combines the ProtT5-XL protein language model with protein-specific evolutionary constraints to predict mutational effects on stability, binding, and epistasis—largely zero-shot.

A Dirichlet flow-matching model that generates family-aware protein sequences by initializing from ancestral-reconstruction lineage priors rather than random noise.

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 mechanistic-interpretability framework that trains sparse autoencoders on protein language model embeddings to extract interpretable features for zero-shot variant effect prediction.

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

Sequence-based discrete-diffusion framework that designs peptide binders with specified agonist or antagonist behavior against GPCR targets.

Multiscale graph neural network for fixed-backbone protein binder sequence design with a contrastive decoding algorithm to improve target selectivity.

A residual VQ-VAE tokenizer that learns a discrete alphabet of protein conformational ensembles from molecular dynamics data, usable as a frozen representation layer for downstream tasks.

An 85M-parameter conditional discrete diffusion model for protein variant generation with a calibrated identity dial to steer similarity to a wild-type sequence.

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.