All Competitors

Diffusion model translating in both directions between protein sequences and fluorescence microscopy images to predict subcellular localization.

Biomolecular structure prediction foundation model covering proteins, small molecules, DNA, RNA, and glycans in a single diffusion framework.

Diffusion model for protein-protein docking that unifies pose sampling and energy-based ranking, works without MSAs, and generalizes to new targets.

Open-source reproduction of AlphaFold 3 that predicts structures of proteins, DNA, RNA, and small-molecule ligands, including their mixed complexes.

Deep learning framework predicting equilibrium distributions of molecular systems, enabling efficient ensemble generation and conformation sampling.

Discrete generative model for antibody protein sequences combining MCMC walks on a smoothed energy landscape with one-step denoising jumps.

Diffusion model for synthesizing single-cell RNA-seq data, with guided generation of specific cell types, rare cells, and developmental trajectories.

Diffusion model for programmable protein design that jointly samples structures and sequences, conditioned on symmetry, shape, or text prompts.

Discrete diffusion model for protein sequence and MSA generation, enabling controllable de novo design directly in sequence space without structure.

De novo protein design diffusion model that generates backbone structures conditioned on binding targets, symmetry constraints, and functional motifs.