All Competitors

Retrieval-augmented framework for de novo peptide binder design that conditions generation on retrieved, structurally aligned binding evidence.

Retrieval-augmented model for matched molecular pair transformations, proposing localized analog edits guided by retrieved reference compounds.

Multimodal single-cell foundation model whose multiway Transformer jointly models scRNA-seq and scATAC-seq from RNA-only, ATAC-only, or paired inputs.

SMILES molecular encoder on a DeBERTaV2 backbone, pretrained on 123M PubChem molecules with physicochemical and structural-similarity objectives.

Transformer that infers whole-genome DNA methylation from gene expression, generalizing zero-shot to unmeasured CpG sites and unseen samples.

Genomic language models fine-tuned to detect and classify antibiotic resistance genes, catching divergent ARGs that reference alignment misses.

Diffusion model that generates continuous-time, all-atom biomolecular trajectories, reproducing conformational kinetics far more cheaply than MD.

Vision-omics finetuning that aligns pathology foundation models with spatial transcriptomics so morphology features predict local gene expression.

Multimodal pre-training distils 3D, text and biochemical knowledge into a 2D molecular graph encoder, so downstream prediction needs only SMILES.

Protein language model that encodes sequences as discrete words from a learned vocabulary for zero-shot function inference and protein design.

Single-cell foundation model that forecasts how cancer cells evolve, autoregressively generating future gene expression from prior cell states.

Latent diffusion model that designs D-peptide binders against native L-protein targets, generalizing across chirality via axial vector features.

Universal all-atom machine-learning force field for molecular dynamics, with ab initio-level accuracy on solvated biomolecules of ~1,500 atoms.

Pan-cancer pretrained diffusion model imputing genome-wide expression from sparse spatial transcriptomics panels, zero-shot and reference-free.

Liquid-biopsy deep learning model that infers transcriptome-wide tumor gene expression from standard-depth cell-free DNA whole-genome sequencing.

Protein-ligand foundation model that maps coarse-grained structural representations directly to binding affinity, running ~26x faster than Boltz-2.

Multimodal generative model predicting viral antigenic change zero-shot from disentangled evolutionary, physicochemical, and structural signals.

Tokenizer-free genomic foundation model that adaptively chunks raw nucleotides, enabling zero-shot variant fitness and gene essentiality prediction.

Cross-species multimodal foundation model of immunology and inflammation, harmonizing transcriptomics and histology into patient-level embeddings.

Vision foundation model for the tree of life, scaling BioCLIP 2 to a ViT-H/14 backbone and more organism images for zero-shot species classification.

Automated sleep staging for polysomnography in Parkinson's disease and isolated REM sleep behaviour disorder, with per-epoch confidence estimates.

Unified drug design engine for protein-ligand structure prediction, binding affinity estimation, and compound generation from Isomorphic Labs.

Vision-language foundation model linking human brain activation maps and neuroscience text for text-to-brain and brain-to-text generation.