All Competitors

Generative model for chemically modified and macrocyclic peptides that builds molecules in HELM notation, supporting de novo design and infilling.

Tumor foundation model that infers whole-transcriptome and microenvironment signal from H&E slides, pretrained on paired spatial transcriptomics.

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

Self-supervised foundation model that embeds cancer genomes from somatic SNVs and copy-number alterations across 33 tumor types for tumor subtyping.

Generative microscopy foundation model that synthesizes in-silico fluorescence images of protein subcellular localization from amino-acid sequence.

Masked discrete-diffusion model over millions of full-length mRNAs, steered by Monte Carlo tree search for joint codon optimization and UTR design.

Transcriptomics foundation model from Recursion that masks and reconstructs RNA-seq gene expression counts to learn reusable sample embeddings.

Tri-modal foundation model unifying histology images, spatial transcriptomics, and language for zero-shot pathology and spatial biology reasoning.

Self-supervised foundation model for continuous glucose monitoring, with dual streams separating slow physiological state from transient events.

Zebrafish sequence-to-function model predicting cell-type-specific gene expression from DNA sequence across embryonic development.

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

Structure-prediction and design engine that turns ESMC sequence representations into all-atom 3D structures of proteins and biomolecular complexes.

Hyperbolic protein language model for alignment-free phylogenetic inference, turning ESM2-650M embeddings into distance matrices for tree placement.

700M-parameter DNA language model pretrained on the rice pangenome, serving as a reusable base model for crop genomics and molecular breeding.

Contrastive promoter-protein pretraining that aligns bacterial promoters with their encoded proteins to learn regulatory genomics representations.

Variant effect predictor pairing a protein language model with family-specific evolutionary constraints to score stability, binding, and epistasis.

Dirichlet flow-matching model for protein design that generates family-aware sequences from ancestral-reconstruction priors, not random noise.

Mixture-of-Experts genomic foundation model for the human microbiome, with 4.7B parameters pretrained on bacterial, archaeal, and phage genomes.

Codon-level mRNA language model adapted from ESM-2 650M by swapping amino-acid tokens for codon tokens, transferring protein knowledge to mRNA tasks.

RNA language model that predicts secondary structure of internal ribosome entry sites from sequence alone, trained on roughly 50,000 IRES sequences.

Spatial transcriptomics foundation model for the tumor microenvironment, giving TME-aware embeddings and in silico perturbation from one checkpoint.

Metabolomic foundation model pretrained on UK Biobank NMR metabolite profiles, reused with a frozen backbone for aging, subtyping, and disease risk.

Promptable DNA language model that generates multi-kilobase plasmid sequences from plain-language component specs, refined with verifiable rewards.

Multimodal framework that detects and localizes DNA lesions from native nanopore signal, built on the damage-aware LesionBERT foundation model.