All Competitors

Cross-modal single-cell foundation model that aligns gene-expression profiles with LLM-enriched cell descriptions in a shared embedding space.

Single-cell foundation model using tabular attention over context cells to predict responses to arbitrary perturbations without fine-tuning.

Transformer foundation model pretrained on a biomedical knowledge graph for zero-shot drug repurposing, target, and adverse-effect prediction.

Generative language model for phenotype-driven drug discovery, proposing small-molecule structures from up- and down-regulated gene signatures.

Transcriptomic foundation model pretrained on 67M single-cell and spatial profiles, modeling gene expression and inter-cellular dependencies.

Spatially aware transcriptomic foundation models for cancer, pairing 50um-Local and 250um-Extended views of spot-resolution spatial transcriptomes.

Microscopy image restoration foundation model unifying 8 tasks across 5 modalities and 2D/3D data, with zero-shot inference on unseen systems.

Generative foundation model that imputes genes and denoises spatial transcriptomics, conditioned on H&E histology, scRNA-seq, and spatial priors.

Protein-family language model trained on unaligned homolog sets for zero-shot variant fitness prediction and design. ProFam-1 holds 251M parameters.

Structure-aware transformer that makes zero-shot, per-adenosine predictions of ADAR-mediated A-to-I RNA editing to guide therapeutic guide-RNA design.

De novo peptide sequencing transformer that reads modified and unmodified peptides directly from tandem mass spectra without a reference database.

Pan-cancer multi-omic foundation model encoding CpG-island DNA methylation and RNA-seq for zero-shot cancer classification and mutation prediction.

Conversational single-cell and spatial multi-omics brain foundation model, with zero-shot cell annotation and disease prediction across species.

Protein sequence encoder that maps ESM2 embeddings to a learned 20-letter alphabet for structure-quality remote homology detection at MMseqs2 speed.

Hierarchical single-cell foundation model that turns scRNA-seq profiles into zero-shot donor-level embeddings for disease and biomarker prediction.

RNA interaction foundation model for conditional, zero-shot design of RNA sequences that bind protein, DNA, or RNA targets without retraining.

Deep learning framework that predicts DNA methylation from genomic sequence across 39 human tissues, with an scRNA-seq variant for unseen cell types.

Cross-species-pretrained CNN that predicts single-CpG DNA methylation from genomic sequence and interprets the cis-regulatory motifs that govern it.

Multimodal foundation model that distills Evo 2 into a compact encoder guided by Hi-C data, predicting cell-type-specific 3D genome architecture.

Histopathology model that predicts single-cell type composition and reconstructs spatial gene expression from H&E slides, with no molecular assay.

Hierarchical DNA foundation model that co-trains a dynamic token-merging tokenizer with latent Transformers to match genomic information density.

Retrieval-augmented protein encoders that fuse homologous sequences into a single-pass transformer for variant effect and contact prediction.

Self-supervised Siamese network for cryo-electron tomography, enabling zero-shot denoising, segmentation, and macromolecule detection in tomograms.

Spectral prediction model for modified peptides, forecasting fragment-ion intensities and retention time with zero-shot generalization to unseen PTMs.