All Competitors

Predicts protein properties from sequence alone by LoRA fine-tuning ESM-2 and ESM-C backbones, with contact maps biasing attention pooling.

Multimodal protein representation model that iteratively fuses a sequence language model with a 3D structure encoder through a shared learnable token.

RNA tertiary structure prediction from sequence alone with a variational autoencoder over distance matrices, reaching 4.5 A RMSE on short RNAs.

De novo atomic model building from cryo-EM density maps, adapting AlphaFold2 with local attention and a 3D rotary position embedding.

Peptide toxicity prediction that fuses frozen ProtT5 residue embeddings with ESMFold-predicted structure in an E(3)-equivariant graph neural network.

Zero-shot variant effect prediction that fuses a frozen protein language model with an equivariant graph network over residue contact graphs.

Multimodal diffusion transformer for de novo protein design, jointly generating sequence and structure conditioned on 465 Gene Ontology functions.

Protein sequence design model that identifies each residue from the voxelized atomic microenvironment around it, reaching 68.33% accuracy on TS500.

Multimodal diffusion protein language model co-generating sequence and structure. Bit-level structure supervision cuts folding RMSD from 5.52 to 2.36.

Protein structure tokenizer that discretizes backbones into 512 discrete tokens and reconstructs all-atom structures, including side chains.

Structure-based Gene Ontology annotation that proposes candidate functional regions in a residue graph before reading them out as GO terms.

Protein foundation model with 3B parameters, pretrained jointly on sequence and 3D structure via masked language modeling and diffusion denoising.

Protein stability predictor scoring ΔΔG for point mutations by fusing ESM-2 embeddings with ProteinMPNN backbone geometry. Wet-lab validated.

Remote-homolog template recognition that threads a sequence against clustered PDB and AlphaFold DB structures to improve AlphaFold2 modelling.

Cyclic peptide structure prediction for sequences carrying unnatural amino acids, adding atom-level features and cyclization-aware position encoding.

Protein structure prediction from general-purpose transformer blocks and flow matching, with no MSAs, pair representations, or triangle attention.

Text-to-protein generative model designing de novo sequences from plain-language function descriptions, with designs confirmed by crystallography.

Protein structure prediction for monomers and complexes in one three-track network, scaling past 1000 residues without triangle attention.

Protein-ligand cofolding model that predicts 3D complex structures with SO(3)-equivariant diffusion, trained on physics-based synthetic data.

Atomic protein model building from cryo-EM density maps, resolving conformational heterogeneity through atom-centric sampling and diffusion.

Protein language model over byte-pair-encoded amino acid tokens, fine-tuned for protein family classification and binary interaction prediction.

Multi-domain protein and complex assembly from deep-learned inter-domain interactions, averaging TM-score 0.922 across 219 multi-domain targets.

Structure-based drug design language model fusing protein structural and evolutionary encoders with SAFE fragment tokens for hit-to-lead generation.