All Competitors

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 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 protein-text foundation model embedding sequences and natural language in a shared token space, enabling protein understanding and de novo design from one checkpoint.

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.

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.

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.

A motif-aware graph diffusion transformer for controllable molecular generation that transfers to unseen properties by learning only lightweight task embeddings with the generator frozen.

A reasoning-guided foundation model for de novo antibody CDR design, pairing a multimodal-LLM understanding expert with a Boltz-1-based diffusion generation expert.

A 110M-parameter multimodal RNA language model that designs RNA sequences from secondary structure, consensus, and Gene Ontology constraints via discrete diffusion.

Modular deep-learning framework for 3D-structure-based RNA sequence design, pairing a direct GNN predictor (SCRU-Seq) and a diffusion model (SCRU-Diff) built on self-contained RNA units.

A Schrödinger-bridge diffusion model for virtual multiplex staining that translates standard H&E histology into multiplex immunohistochemistry images.

Enhanced 464M-parameter version of Protenix with substantial gains in antibody-antigen complex prediction over v1, plus target-conditioned VHH-Fc generative design with up to 48% hit rates.

Multimodal diffusion model that co-designs protein sequence and 3D structure around arbitrary biomolecules, demonstrated by designing novel heme enzymes catalyzing carbene-transfer reactions.

Generative discrete-diffusion model that designs regulatory DNA with tunable activity and learns activity-predictive representations rivaling genomic language models.

Deep generative model simulating fluorescence microscopy images for all 12,800 human proteins across three landmark stains, providing proteome-wide virtual cell imaging at single-cell resolution.

Generates single-cell transcriptomic profiles from structured biological metadata via contrastive language-omics pretraining and a diffusion transformer.

A generative cellular world model that uses masked discrete diffusion to learn whole-transcriptome scRNA-seq distributions and simulate perturbation responses across tissues and species.

Transformer-based protein language diffusion model generating all-atom intrinsically disordered protein conformational ensembles, validated against experimental NMR and SAXS data.