All Competitors

Flow-matching model that jointly samples 3D de novo molecules and several low-energy conformers, extending to pocket-conditioned ligand design.

Diffusion model for structure-based drug design that jointly generates 3D ligands and holo pocket conformations from an apo protein structure.

Pretrained language model for 3D molecule generation in protein pockets, unifying de novo and fragment-based drug design in one multi-task framework.

Guided discrete diffusion model for antibody lead optimization, conditioning sequence design on the seed binder's CDR canonical backbone conformation.

Denoising diffusion bridge model for peptide binder design that generates ligand surfaces and backbones complementary to a target receptor surface.

Protein dynamics model that samples conformational ensembles autoregressively at slow and fast timescales, generalizing zero-shot to unseen proteins.

Equivariant diffusion model that converts peptide binders into drug-like small molecules, generating peptidomimetics inside the target protein pocket.

Genomics foundation model that unifies sequence-to-function prediction, DNA language modeling, and generative regulatory design in one backbone.

Multi-target drug discovery framework pairing a diffusion-transformer generator with evolutionary latent-space search and synthesis-aware scoring.

Latent diffusion model for generating single-cell gene expression profiles, pairing a permutation-invariant autoencoder with a diffusion transformer.

Single-cell latent diffusion model fine-tuned on 14.5 million CD4+ T cells to simulate transcriptomic effects of single-gene perturbations.

Structure-based drug design model that generates ligands for a protein pocket, pairing a diffusion structure encoder with preference optimization.

Enzyme backbone design model that adds substrate and catalytic-site control to a pretrained SE(3) flow-matching generator via a lightweight adapter.

All-atom generative model for de novo protein design using SE(3) flow matching over oriented residue rigid bodies.

Flow-matching model that predicts protein conformational ensembles across the order-disorder continuum, from folded domains to disordered chains.

Molecular docking model that predicts protein-ligand binding poses with multi-stage Riemannian flow matching, yielding physically valid geometry.

Retrieval-augmented latent diffusion model for protein binder design, retrieving interfaces in a shared latent space across peptides and antibodies.

Protein structure autoencoder compressing backbone coordinates into a latent space, paired with a latent diffusion model for generative design.

Autoregressive protein language model for antibody Fc domains, reinforcement-tuned to design variants with programmable Fc-receptor binding profiles.

Diffusion model over coarse-grained protein topology that samples diverse folds, handing them to RFdiffusion to build de novo protein backbones.

De novo ligand design framework that generates protein binders and small molecules by inverting gradients through a differentiable docking model.

GPCR structure prediction and peptide design model that generates linear and cyclic peptide agonists carrying noncanonical amino acids, zero-shot.

Flow-based protein structure tokenizer, a diffusion autoencoder replacing SE(3)-invariant components with global coordinates and standard attention.

Distilled few-step protein backbone generator that adapts Score Identity Distillation to Proteina for over 20x faster de novo structure sampling.