All Competitors

Variable-length generative protein design across structure, sequence, motif scaffolding, and peptide co-design via a generalized Poisson flow.

Generative framework that learns a developmental vector field from scRNA-seq snapshots, coupling flow matching with molecular RNA kinetics.

Generative protein-dynamics model that predicts short molecular dynamics trajectories with rectified flow matching over residue frames and torsions.

Flow-matching framework that translates omics signatures across biological domains, such as mouse to human transcriptomics, without paired samples.

Dirichlet flow-matching model for protein design that generates family-aware sequences from ancestral-reconstruction priors, not random noise.

Protein structure prediction and binder design in a single generative step, replacing AlphaFold3's iterative diffusion sampling with one forward pass.

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

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

Protein backbone design model pairing flow matching with a Mamba state-space backbone, generating long proteins in linear time with exact geometry.

Virtual cell foundation model predicting single-cell responses to genetic, chemical, and cytokine perturbations with conditional flow matching.

Flow-matching generative model for de novo atomistic protein binder design against protein and small-molecule targets, including carbohydrate binders.

Edit-based flow-matching model that proposes protein variants by learning insertions, deletions, and substitutions on a template sequence.

Neural Hamiltonian flow for protein sequence generation with inference-time control over composition and net charge via analytical bias potentials.

Self-supervised SE(3) geometric pretraining for protein backbone generators, improving designability, motif scaffolding, and conformational ensembles.

Flow-matching generative model for peptide sequence design that learns the protein semantic distribution, fine-tuned for antimicrobial peptides.

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

Single-cell perturbation prediction model using conditional flow matching to map control cells to perturbed expression distributions.

Latent flow-matching method that repurposes protein language model embeddings to generate high-fitness protein variants without predictor guidance.

Pan-cancer model predicting spatial gene expression from H&E histology using conditional flow matching with a mixture-of-experts velocity field.

Partially latent flow-matching model for de novo protein design, jointly generating sequence and all-atom structure for proteins up to 800 residues.

Flow-matching generative model for de novo protein binder backbone design, built on a Pairformer architecture with in silico interface maturation.

Flow-matching model for therapeutic peptide design that co-designs sequence, structure, and molecular surface to disrupt protein-protein interactions.

Structure-based drug design model that unifies de novo generation, docking, conformer generation, and pharmacophore conditioning via flow matching.

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