All Competitors

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

Generative foundation model for antibody and multispecific design, doubling its predecessor's experimental success rate on therapeutic targets.

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

Cryo-EM ligand modeling pipeline that detects bound ligand densities in a map, then reconstructs their atomic structures with a diffusion model.

Generative model for chemically modified and macrocyclic peptides that builds molecules in HELM notation, supporting de novo design and infilling.

Generative microscopy foundation model that synthesizes in-silico fluorescence images of protein subcellular localization from amino-acid sequence.

Masked discrete-diffusion model over millions of full-length mRNAs, steered by Monte Carlo tree search for joint codon optimization and UTR design.

Physics-informed generative foundation model for quantitative diffusion MRI that maps brain microstructure and adapts zero-shot to each participant.

Diffusion transformer for virtual tissue synthesis, generating H&E histopathology patches conditioned on spatial gene expression and morphology.

Cell world model pretrained on a 2.4M-cell mouse embryonic atlas, predicting one-step transcriptional state transitions and perturbation response.

Structure-prediction and design engine that turns ESMC sequence representations into all-atom 3D structures of proteins and biomolecular complexes.

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

Generative model that reconstructs single-cell spatial coordinates from scRNA-seq guided by spatial transcriptomics, without cell-type labels.

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

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

370M-parameter ligand-conditioned discrete diffusion model that co-designs protein sequence and structure under explicit small-molecule constraints.

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

Conditional discrete diffusion model for protein variant generation, with a calibrated identity dial controlling drift from a wild-type sequence.

Latent diffusion model for PTM-aware protein sequence design, using ControlNet-style conditioning to steer generation toward chosen PTM sites.

RNA inverse-folding language model that designs nucleotide sequences satisfying a target secondary structure, fixed bases, and coding constraints.

Discrete diffusion model for conditional antibody sequence design with germline-absorbing noising that focuses learning on somatic variation.

All-atom protein co-design model that generates sequence and structure together in one unified diffusion process, aimed at hard binder design.

Three fixed ProtGPT2 fine-tunes specialized for metalloprotein generation, trained on ProteinMPNN-derived synthetic sequences.

Co-generative protein language model decoding sequence and structure tokens together from GO functional annotations for de novo protein design.