All Competitors

A generative protein-dynamics model that predicts short-horizon MD trajectories using rectified flow matching in velocity space over residue frames and torsions.

A constrained deep flow-matching framework for distributional translation of omics signatures across biological domains, such as mouse-to-human transcriptomics, without paired samples.

A Dirichlet flow-matching model that generates family-aware protein sequences by initializing from ancestral-reconstruction lineage priors rather than random noise.

A single-step generative model for protein structure prediction and binder design that reaches AlphaFold3-level accuracy with a claimed ~15x inference speedup.

Latent flow-matching foundation model that predicts pan-cancer spatially-resolved single-cell gene expression directly from routine H&E histology slides.

A generative protein-protein docking model that adapts AlphaFold-Multimer via flow matching, replacing the template module with a docking module.

Virtual cell foundation model pairing LLaMA-based cellular encoding with set-aware conditional flow matching to predict single-cell perturbation responses at atlas scale.

Physics-informed generative model that pairs flow matching with a Mamba state-space backbone for linear-time protein backbone design, scaling to 2,000+ residues.

Partially latent flow-matching generative model for de novo atomistic protein binder design against protein and small-molecule targets, with experimental validation at million-design scale.

Edit-based flow-matching model that generates variable-length protein variants via learned insertions, deletions, and substitutions on a template sequence.

A neural Hamiltonian flow that generates protein sequences with continuous, inference-time control over composition and net charge via analytical bias potentials—no retraining required.

Rigidity-aware geometric pretraining framework that front-loads SE(3) geometry learning to improve protein backbone generation, motif scaffolding, and conformational ensemble modeling.

A flow-matching generative model for peptide sequence design that learns the protein semantic distribution, with antimicrobial-peptide fine-tuning.

A multimodal generative model that learns disentangled evolutionary representations to predict viral antigenic change, generalizing zero-shot across viral families.

Distributional flow matching model for single-cell perturbation prediction that models population-level expression shifts using a graph-aware differential-attention transformer.

A latent flow-matching method that repurposes protein language model embeddings to generate high-fitness protein variants without predictor guidance during sampling.

A pan-cancer generative model that predicts spatial gene expression from H&E histology using conditional flow matching with a mixture-of-experts velocity field.

Partially latent flow-matching model for joint generation of protein amino-acid sequence and full atomistic structure (backbone plus side chains) for proteins up to 800 residues.

A Pairformer-based flow-matching generative model for de novo protein binder backbone design, paired with in silico maturation to reach picomolar-to-nanomolar affinities.

Multi-modal flow-matching model that co-designs the sequence, structure, and molecular surface of therapeutic peptides targeting protein-protein interactions.

A pretrained multi-task flow-matching model for structure-based drug design, unifying de novo design, docking, conformer generation, and pharmacophore conditioning.

Atom-level generative diffusion model for de novo enzyme design. Scaffolds arbitrary functional group geometries, solving all 41 benchmark active sites vs. 16/41 for prior methods.

Structure-conditioned protein sequence design model combining a RoseTTAFold-like three-track architecture with discrete flow matching for fast, few-step inverse folding.

Flow-matching generative model that simulates high-resolution fluorescence images of human fibroblasts conditioned on surface micro-topographies, a digital twin of cell-material interactions.