All Competitors

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

A conditional encoder-decoder language model that designs RNA sequences under simultaneous secondary-structure, fixed-base, and coding constraints.

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.

Protein sequence design method that explicitly models small molecules, nucleotides, and metals at atomic resolution, enabling ligand-aware design with 100+ validated designs.

A 7-billion-parameter encoder-only DNA foundation model trained on 10.6 billion nucleotides from 796 species for functional genomics and synthetic biology.

A 1.6-billion-parameter RNA foundation model pretrained on 42 million non-coding RNA sequences, achieving state-of-the-art performance across 24 of 26 RNA understanding benchmarks.

A 7B parameter genomic foundation model using StripedHyena architecture to model prokaryotic DNA, RNA, and proteins at single-nucleotide resolution with 131k token context.

A transformer language model pretrained on 5' UTR sequences across five species to predict mRNA translation efficiency, ribosome loading, and expression levels.

A VAE-based generative model that designs novel functional RNA sequences by encoding MSA and consensus secondary structure constraints from Rfam families.

Zero-shot antibody affinity maturation using ESM pseudolikelihood scoring. Improves binding up to 160-fold with no antigen-specific training data.

Message passing neural network for fixed-backbone protein sequence design. Achieves 52.4% native sequence recovery, far surpassing Rosetta's 32.9%.