All Competitors

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

Multimodal foundation model integrating spatial transcriptomics, H&E histopathology, and pathway scores for single-cell niche discovery.

Coding-sequence foundation model for mRNA design, pretrained as a BART denoising encoder-decoder on mRNA from nine taxonomic groups.

Vision transformer trained with DINO self-supervision to segment micronuclei in DNA-stained fluorescence images across cell lines and microscopes.

DNA language model that replaces fixed tokenization with conservation-guided patching, letting models up to 10x smaller match top genomic benchmarks.

Cell-centric microscopy foundation model that distills morphology and microenvironment views into a unified embedding for virtual spatial omics.

Paired-sequence protein language model that jointly encodes two interacting chains to predict interactions, binding affinity, and interface contacts.

Genomic foundation model for Cypriniformes fish, built on a Mamba-2 state space model with a 32 kb context window for long-range genome modeling.

Bacterial proteome foundation model that learns contextualized gene and whole-genome representations from tens of thousands of complete genomes.

Self-supervised transformer for population genetics, pretrained on 1000 Genomes data, that detects positive selection via haplotype-wise attention.

Generative single-cell foundation model trained on 100M+ transcriptomes that predicts how genetic perturbations reshape cell trajectories over time.

DNA foundation model using masked discrete diffusion to unify bidirectional sequence understanding and de novo generation in one architecture.

Self-supervised SE(3) geometric pretraining for protein backbone generators, improving designability, motif scaffolding, and conformational ensembles.

Graph-transformer foundation model pretrained on 3M protein pockets and 5M molecules as E(3)-equivariant graphs for protein-ligand representation.

Multimodal foundation model pretrained on ~500K unlabeled PubChem molecules that jointly predicts nine thermophysical properties of small molecules.

Foundation model for 3D genome architecture, using masked locus modeling over genome-wide contact profiles to capture chromosome-scale organization.

Knowledge-graph-grounded model that predicts single-cell transcriptomic responses to small molecules, with zero-shot prediction for unprofiled drugs.

Plant genomic foundation models from 0.1B to 1B parameters, pretrained on 43 phylogenetically diverse plant genomes for variant effect prediction.

Polarizable machine-learning interatomic potential extending MACE with long-range electrostatics, trained on 100M OMol25 DFT calculations.

Protein evolution model that learns indel dynamics and epistasis from unaligned sequences, simulating trajectories that yield functional proteins.

Genomic foundation model training framework whose joint-embedding predictive objective learns functional representations of masked DNA, not tokens.

BERT-style language model for somatic mutations, pretrained on cancer sequencing from 210,000+ patients for tumor subtyping and therapy response.

Multimodal single-cell foundation model whose multiway Transformer jointly models scRNA-seq and scATAC-seq from RNA-only, ATAC-only, or paired inputs.

SMILES molecular encoder on a DeBERTaV2 backbone, pretrained on 123M PubChem molecules with physicochemical and structural-similarity objectives.