All Competitors

E(3)-equivariant diffusion model for macrocycle design that turns acyclic molecules into macrocycles, with a transformer choosing where to cyclize.

Protein backbone generator running a diffusion transformer over SaProt structural tokens, with an IPA token cache to speed up de novo design.

Full-atom SE(3)-equivariant diffusion model that inpaints binding interfaces to design proteins that bind DNA, RNA, and small molecules.

Single-cell foundation model built on masked discrete diffusion, jointly generating gene identities and expression values from 59 million cells.

Bidirectional generative framework linking nuclear morphology and gene expression, built on a morphology model trained on 21 million segmented nuclei.

Structure-free protein-ligand binding affinity predictor built on OpenFold3 that scores potency from a protein sequence and a ligand SMILES string.

Zero-shot peptide binder designer that runs diffusion in a pretrained protein embedding space, proposing binders without structure prediction.

Evolution-guided diffusion model that generates temporal protein folding pathways, from unfolded chain to native state, rather than static structures.

Diffusion model for de novo AAV capsid design that steers sampling with a viability classifier toward assemblable, packaging-competent variants.

Sequence-only latent diffusion model that designs target-specific peptide binders, cascaded with an affinity classifier through joint optimization.

Generative foundation model that imputes genes and denoises spatial transcriptomics, conditioned on H&E histology, scRNA-seq, and spatial priors.

All-atom protein design diffusion model conditioned on ligands, nucleic acids, and other non-protein atoms, supporting enzyme and DNA binder design.

De novo protein binder design suite from ByteDance pairing diffusion and hallucination generators with confidence-based filtering of designs.

Multimodal protein language model that adds a continuous-token diffusion head to a discrete pLM, modeling structure without vector quantization.

Energy-based model of protein conformational space, turning a diffusion model into a statistical potential for structure ranking and mutation scoring.

Atom-level diffusion model for de novo enzyme design that scaffolds arbitrary active-site geometries without specifying catalytic residue positions.

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

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

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.

Diffusion model for structure-based drug design that jointly generates 3D ligands and holo pocket conformations from an apo protein structure.

Guided discrete diffusion model for antibody lead optimization, conditioning sequence design on the seed binder's CDR canonical backbone conformation.

Denoising diffusion bridge model for peptide binder design that generates ligand surfaces and backbones complementary to a target receptor surface.

Equivariant diffusion model that converts peptide binders into drug-like small molecules, generating peptidomimetics inside the target protein pocket.