All Competitors

Autoregressive DNA foundation model for variant effect prediction, using 6-mer tokenization to match Evo2-7B win rates at far higher throughput.

Motif-aware graph diffusion model for controllable molecular generation that adapts to unseen properties by learning a lightweight task embedding.

Co-generative protein language model decoding sequence and structure tokens together from GO functional annotations for de novo protein design.

Self-supervised brain MRI foundation model built on DINOv3, pretrained on roughly 6.6 million unlabeled axial slices for neuroimaging tasks.

Phylogeny-aware genome annotation model predicting exons, introns, UTRs and repeats directly from eukaryotic DNA, with no RNA or protein evidence.

Virtual spatial transcriptomics foundation model predicting pan-cancer, spatially-resolved single-cell gene expression from H&E histology slides.

Generative model for self-tracked menstrual health data, encoding cycles and symptom logs as token sequences for synthetic cohorts and forecasting.

Multi-modal transformer fusing LLM gene embeddings with biological knowledge graphs to predict single-cell responses to genetic perturbations.

Meta-learning framework that transfers perturbation responses across cell lines, donors, and drugs from a few measured seed perturbations.

Generative multimodal foundation model spanning DNA, RNA, and protein, with any-to-any inference across genome, transcriptome, and proteome.

Direction-aware foundation model trained on bulk RNA-seq differential-expression profiles to simulate coordinated gene dynamics in viral infection.

Protein-protein docking model adapting AlphaFold-Multimer with a docking module and flow-matching training to assemble subunits without MSAs.

110M-parameter RNA language model that designs sequences from secondary structure, motif, and Gene Ontology constraints via discrete diffusion.

Autoregressive nucleotide-and-text foundation model generating DNA and RNA sequences from natural-language prompts that name species and function.

Pathology foundation model that infers spatial transcriptomics and proteomics directly from routine H&E whole-slide images, with no spatial assay.

Single-cell foundation model that tokenizes scRNA-seq into 10 tokens in a Qwen3-4B vocabulary for cell type annotation and perturbation prediction.

Leakage-controlled multimodal model predicting within-species relative protein expression across 385 bacterial species, with transfer to unseen phyla.

Transcriptome foundation model for precision oncology, generalizing zero-shot across tissue, plasma cfRNA, and tumor-educated platelet modalities.

Genomic foundation model for rice, pretrained on 422 Oryza genomes with a 1 Mbp context window and a 1.25B-parameter mixture-of-experts transformer.

Long-context RNA foundation model that predicts splicing, isoform abundance, and variant effects from 64 kb of unspliced pre-mRNA sequence.

Chemical language model that generates matched molecular pair transformations from SMILES and SMARTS to design medicinal-chemistry analogs.

Single-pass RNA inverse folding: a graph neural network predicts a nucleotide sequence from a target 3D backbone in constant time.

Diffusion-based RNA inverse folding, denoising toward a nucleotide sequence conditioned on a target 3D backbone for higher native sequence recovery.