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.

Conditions a pretrained DNA sequence embedding on CpG methylation to predict gene regulation across cell types and alleles, generalizing zero-shot to imprinting, X-inactivation, and accessibility.

A VAE trained on scRNA-seq reference data and applied frozen at inference to impute unmeasured genes and denoise spatial transcriptomics profiles.

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

Specificity Foundation Model that predicts CRISPR gRNA off-target DNA specificity from sequence using a physics-derived dual-encoder with symmetric contrastive learning.

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.

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

Specificity Foundation Model that predicts microRNA-mRNA target 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 graph foundation model for fMRI brain networks, pretrained across 27 datasets with graph and language prompts for zero/few-shot generalization to unseen disorders.

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.

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

A physics-informed generative foundation model for quantitative diffusion MRI that maps brain microstructure (tensor, kurtosis, NODDI) and adapts zero-shot to each participant's data.

A diffusion transformer that synthesizes H&E histopathology image patches conditioned jointly on spatial transcriptomics gene expression and morphological embeddings.

Genetically aligned foundation model for blood smear cytology that links single white-blood-cell morphology to chromosomal aberrations and mutations for AML/APL diagnosis.

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 4.7B-parameter Mixture-of-Experts genomic foundation model pretrained on ~1.2 trillion nucleotide tokens from human-associated microbial genomes.

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.

A spatial-transcriptomics foundation model for the tumor microenvironment that produces TME-aware embeddings and enables in silico perturbation from a fixed pretrained checkpoint.

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

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