All Competitors

Specificity Foundation Model that predicts small-molecule drug-target protein specificity from sequence using a physics-derived dual-encoder with symmetric contrastive learning.

Specificity Foundation Model that predicts enzyme-substrate 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 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.

A cross-modal transfer-learning model that adapts the ESM-2 650M protein language model to mRNA analysis by swapping amino-acid tokens for codon tokens, applied to mRNA benchmarks without re-training.

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.

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 latent diffusion model with ControlNet-style conditioning for post-translational-modification-aware protein sequence design.

Discrete diffusion model for conditional antibody sequence generation that restricts learning to somatic variation via a germline-absorbing noising process.

Contrastively fine-tuned ESM-2 (35M and 150M) protein language models that produce general-purpose sequence embeddings where biological similarity maps to embedding proximity.

Leakage-controlled multimodal model predicting within-species relative protein expression across 385 bacterial species, with transfer to unseen phyla.

SMILES-based chemical language models pretrained on 100M+ molecules to natively represent therapeutic peptide chemistry, including non-canonical residues.

A multimodal deep-learning framework that fuses sequence, structure, text, and interaction embeddings to predict Gene Ontology function annotations, reaching state of the art on CAFA3.

A CLIP-style dual-encoder model that learns a shared peptide-spectrum representation for zero-shot peptide-spectrum-match inference in DIA proteomics.

A graph-attention model producing context-aware protein embeddings from protein-protein interaction, co-expression, and tissue networks, with biologically motivated data splits.

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

A multimodal reasoning LLM that fuses protein-language-model embeddings with biological context to generate interpretable reasoning traces for protein function and GO-term annotation.

Dual-encoder contrastive model that embeds protein domains and peptides into a shared space to predict domain-peptide binding specificity at proteome scale.

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.

Protein language model that predicts RNA-binding domains, global RNA-binding propensity, and mutation effects at single-residue resolution from sequence.

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