All Competitors

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

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

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

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

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

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

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

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

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.

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

Protein function prediction model that fuses sequence, structure, text, and interaction embeddings with learned gating to assign Gene Ontology terms.

Chemical language models pretrained on SMILES for therapeutic peptides, natively representing non-canonical residues, cyclization, and conjugation.

OpenAI's frontier reasoning model for life-sciences research, tuned for multi-step workflows in protein engineering, genomics, and drug discovery.

Contrastive dual-encoder model for DIA proteomics, embedding peptides and spectra in a shared space for zero-shot peptide-spectrum matching.

Encoder-decoder Transformer that generates intrinsically disordered protein sequences conditioned on target conformational-ensemble descriptors.

Molecular linker design model fine-tuned from Llama 3 that emits PROTAC and fragment linkers as SMILES from natural-language geometry prompts.

Generative pipeline for epitope-targeted de novo antibody (nanobody) CDR design that yields nanomolar binders from only dozens of designs per antigen.

Transcriptomics-native single-cell foundation model that learns batch-invariant cell representations and probabilistically generates virtual cells.

Generative DNA foundation model trained on 91.7M nucleotide sequences and annotations for species classification and mutation effect prediction.

Autoregressive language model trained on 37 million intrinsically disordered region sequences, generating IDRs given flanking folded domains.

464M-parameter structure prediction and design model that improves antibody-antigen complex accuracy over Protenix-v1 and adds generative VHH design.

Multimodal diffusion model that co-designs protein sequence and 3D structure around cofactors and small molecules for de novo heme enzyme design.

Spatial transcriptomics foundation model pairing gene expression with H&E histology for spatial domain discovery and clinical outcome prediction.