All Competitors

Single-cell foundation model adapting LLaMA-3.1-8B with LoRA, recasting transcriptomes and protein interaction networks as natural-language Q&A pairs.

Retrieval-augmented diffusion model for protein inverse folding that conditions sequence generation on profiles from structurally similar homologs.

Mutation effect prediction at protein–DNA and protein–RNA interfaces, combining frozen ESM-2 embeddings with an edge-aware atomic graph network.

Spatial transcriptomics language model that reads tissue as spatial sentences to simulate cell profiles and run in silico perturbations.

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

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

Multimodal protein representation model that iteratively fuses a sequence language model with a 3D structure encoder through a shared learnable token.

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

Bidirectional state-space (Mamba-2) genomic model for ultra-long extrachromosomal circular DNA, scaling linearly with sequence length.

All-atom generative model for de novo protein and peptide binder design against diverse biomolecular targets, wet-lab validated across 26 targets.

Small-molecule structure elucidation from tandem mass spectra, generating SMILES by conditional diffusion guided by fragmentation patterns.

mRNA language model that tokenizes UTRs by nucleotide and coding regions by codon, aligned to protein sequence space through contrastive learning.

Conditional codon language model with 150M parameters that generates species-optimized coding sequences from a protein and its taxonomic lineage.

Flow-matching generative model that synthesizes fluorescence images of human fibroblasts conditioned on surface micro-topographies.

Generative framework that reconstructs missing spatial transcriptomics regions by jointly predicting cell locations, cell types, and gene expression.

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

Generalist neuroimaging vision foundation model pretrained on 5.24M clinical MRI and CT volumes for radiologic diagnosis and report generation.

Flow-matching model that jointly samples 3D de novo molecules and several low-energy conformers, extending to pocket-conditioned ligand design.

Gene expression prediction model combining DNA sequence with Hi-C contact maps to capture 3D chromatin looping behind cell-type-specific expression.

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

Multi-modal contrastive model that aligns H&E histopathology with spatial transcriptomics across tissue scales to predict gene expression from images.

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.