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.

Uncertainty-aware diffusion model that enhances cryo-EM density maps while estimating voxel-wise confidence via Monte Carlo sampling.

Diffusion-based backbone generation and sequence design method for programmable asymmetric transmembrane beta-barrel nanopores.

A two-stage deep learning framework that detects ligand densities in cryo-EM maps and reconstructs their atomic structures with a diffusion generative model.

A deep generative decoder that infers microRNA expression directly from bulk or single-cell mRNA gene expression via a shared mRNA/miRNA latent space.

A hierarchical three-stage cascade that generates chemically modified and macrocyclic peptides in HELM notation, supporting de novo design and constrained infilling.

Channel-adaptive autoregressive generative model that synthesizes in-silico fluorescence microscopy of protein subcellular localization from amino-acid sequence and cellular landmark stains.

A masked discrete-diffusion model over millions of full-length mRNAs, guided by Monte Carlo Tree Search for joint codon optimization and de novo UTR design.

A physics-informed generative foundation model for quantitative diffusion MRI that maps brain microstructure (tensor, kurtosis, NODDI) and adapts zero-shot to each participant's data.

A diffusion transformer that synthesizes H&E histopathology image patches conditioned jointly on spatial transcriptomics gene expression and morphological embeddings.

A cell world model pretrained on a 2.4M-cell mouse embryonic atlas that predicts one-step transcriptional state transitions and transfers to perturbation prediction.

Biohub's structure-prediction and design engine that turns ESMC sequence representations into atomic-resolution 3D structures of proteins and biomolecular complexes.

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

Geometry-first generative framework that reconstructs single-cell spatial coordinates by integrating scRNA-seq with spatial transcriptomics, without cell-type labels.

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

A ligand-conditioned masked discrete diffusion model that co-designs protein sequence and structure under explicit small-molecule constraints.

Sequence-based discrete-diffusion framework that designs peptide binders with specified agonist or antagonist behavior against GPCR targets.

Multiscale graph neural network for fixed-backbone protein binder sequence design with a contrastive decoding algorithm to improve target selectivity.

An 85M-parameter conditional discrete diffusion model for protein variant generation with a calibrated identity dial to steer similarity to a wild-type sequence.

A latent diffusion model with ControlNet-style conditioning for post-translational-modification-aware protein sequence design.

Bayesian deep generative model that integrates gene regulatory networks into RNA velocity inference, enabling cell fate mapping and in silico perturbation of transcription factors.

A conditional encoder-decoder language model that designs RNA sequences under simultaneous secondary-structure, fixed-base, and coding constraints.

Discrete diffusion model for conditional antibody sequence generation that restricts learning to somatic variation via a germline-absorbing noising process.

A fully atomic protein co-design model using unified multimodal diffusion to jointly refine atom types and coordinates in a single stage, with support for non-canonical amino acids.