All Competitors

Fine-tuned Enformer derivative that predicts discrete, interpretable cis-regulatory element class annotations (enhancer, promoter, insulator) directly from DNA sequence across human cell types.

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 reasoning LLM fine-tuned on clinical antimicrobial-susceptibility data augmented with mechanistic rationales, predicting susceptibility with explanations for novel isolate-antibiotic pairs.

Uncertainty-aware diffusion model that enhances cryo-EM density maps while estimating voxel-wise confidence via Monte Carlo sampling.

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

Pathology vision foundation model adapting DINOv3-style self-supervised learning to whole-slide histopathology across continuous magnifications and scales.

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.

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

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

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

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

A two-stage deep learning framework that detects ligand densities in cryo-EM maps and reconstructs their atomic structures with a diffusion generative model.

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

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

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 peptide-MHC binding specificity from sequence using a physics-derived dual-encoder with symmetric contrastive learning.

A 120M-parameter genomic foundation model that learns adaptive DNA token boundaries via H-Net-style dynamic chunking instead of fixed k-mer or byte-pair tokenization.

Multimodal foundation model pretrained on 1.76 billion paired histology-spatial transcriptomics spots, linking whole-slide images to spatial molecular programs.

A deep generative decoder that infers microRNA expression directly from bulk or single-cell mRNA gene expression via a shared mRNA/miRNA latent space.

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.

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.

Multimodal tumor foundation model trained on paired H&E histology and spatial transcriptomics to infer whole-transcriptome and tumor-microenvironment signal from routine H&E alone.