All Competitors

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

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

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 zebrafish DNA sequence-to-function model predicting cell-type-specific single-cell expression across 85 cell-type x developmental-timepoint combinations during embryogenesis.

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.

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

A co-generative protein language model that jointly decodes sequence and structure tokens from GO functional annotations for de novo functional protein design.

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.

An autoregressive nucleotide-and-text foundation model pretrained on ~416B characters from 2,221 eukaryotic species for natural-language-guided conditional generation of DNA and RNA sequences.

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

An encoder-decoder Transformer that generates intrinsically disordered protein sequences conditioned on target conformational-ensemble biophysical descriptors.

A Llama 3 model fine-tuned on a ChEMBL corpus that designs molecular linkers from natural-language geometry and physicochemical prompts, without task-specific re-training.

Generative pipeline for epitope-targeted de novo antibody (nanobody) CDR design that yields nanomolar binders from only dozens of designs per antigen.

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.

A 730M-parameter protein foundation model that co-designs enzyme sequence and 3D structure under small-molecule ligand guidance for de novo enzyme design.

Physics-informed generative model that pairs flow matching with a Mamba state-space backbone for linear-time protein backbone design, scaling to 2,000+ residues.