All Competitors

Diffusion-based generative RNA model for de novo sequence design, conditioned on function, RNA family, structure, or binding proteins.

860M-parameter generative single-cell foundation model that jointly represents and generates epigenomic, transcriptomic, and proteomic modalities.

Bulk RNA-seq foundation model learning normalization-robust transcriptome representations via TF-IDF gene ordering and masked gene modeling.

Gene regulation model that conditions a pretrained DNA sequence embedding on CpG methylation to capture cell-type and allele-specific regulation.

Multi-sequence spine MRI foundation model with DINOv3 encoders, supporting condition classification, pathology localization, and report generation.

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

Transcription factor-DNA binding specificity prediction from sequence, with a physics-derived dual-encoder trained by symmetric contrastive learning.

Specificity foundation model predicting small-molecule drug-target binding from sequence, scored as cross-modal retrieval without docking or assays.

Generative foundation model for antibody and multispecific design, doubling its predecessor's experimental success rate on therapeutic targets.

CRISPR off-target prediction model that scores gRNA-DNA specificity from sequence, framing guide-target recognition as cross-modal retrieval.

Enzyme-substrate specificity model that scores catalytic pairs from sequence with a physics-derived dual-encoder and a contrastive objective.

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

Peptide-MHC binding specificity model that frames presentation as cross-modal retrieval, aligning peptide and MHC encoders by contrastive learning.

Foundation model that predicts microRNA-mRNA target specificity from sequence, using a dual-encoder trained with a symmetric contrastive objective.

Genomic foundation model with 120M parameters that learns adaptive DNA token boundaries by dynamic chunking, not fixed k-mer or byte-pair tokens.

Multimodal foundation model pretrained on 1.76B histology and spatial transcriptomics spots, inferring molecular state from whole-slide images.

Graph foundation model for fMRI brain networks, pretrained across 27 datasets with graph and language prompts for zero-shot disorder classification.

Tumor foundation model that infers whole-transcriptome and microenvironment signal from H&E slides, pretrained on paired spatial transcriptomics.

Protein representation learning from cryo-EM density maps, transferring to flexibility, active-site, binding-affinity, and stability tasks.

Self-supervised foundation model that embeds cancer genomes from somatic SNVs and copy-number alterations across 33 tumor types for tumor subtyping.

Generative microscopy foundation model that synthesizes in-silico fluorescence images of protein subcellular localization from amino-acid sequence.

Masked discrete-diffusion model over millions of full-length mRNAs, steered by Monte Carlo tree search for joint codon optimization and UTR design.

Transcriptomics foundation model from Recursion that masks and reconstructs RNA-seq gene expression counts to learn reusable sample embeddings.

Protein-text foundation model placing amino acid sequences and natural language in one token space for protein understanding and de novo design.