All Competitors

Structure-conditioned graph transformer trained with masked language modeling to learn residue encodings for inverse folding and antibody design.

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

Multimodal protein foundation model spanning sequence, structure, function, and evolution, used for de novo design and structure prediction.

De novo ligand design from a target protein sequence, writing SMILES autoregressively over a merged protein-ligand BPE vocabulary.

All-atom peptide conformational sampling from sequence, using hypernetwork-conditioned diffusion trained by energy against a molecular force field.

Enzyme optimum pH prediction from sequence, ensembling a light-attention network and a support vector regression over frozen ESM-1v embeddings.

Equivariant heterogeneous graph network that rescores docked protein-ligand poses for virtual screening and ranks structural analogs by activity.

Sequence-pair interaction classifier fine-tuned from ProtBERT-BFD, trained against synthetic negatives generated by BLOSUM62-guided mutation.

Zero-shot protein conformation sampling that perturbs an input structure and anneals it back with a score network trained only on crystal structures.

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

Protein model quality assessment predicting per-residue lDDT for monomer and multimer interface models from graph-coupled ESM embeddings.

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

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

Rigid protein-protein docking by diffusion over the rigid-body pose, with a confidence model ranking sampled complexes. Median C-RMSD 4.85 on DIPS.

Protein structure encoder pretrained by contrastive alignment to a frozen protein language model, anchored by self-supervised contact-map prediction.

Joint sequence-structure protein representation framework that fuses ESM-2 language model embeddings with GearNet geometric graph neural networks.

Structure-based drug design by SE(3)-equivariant diffusion over 3D atom coordinates and types, with the same frozen network scoring binding affinity.

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

AlphaFold fine-tuned on peptide-MHC and protein-peptide binding data for specificity prediction across MHC class I/II, PDZ, and SH3 domains.

Cyclic peptide structure prediction and de novo macrocycle design, by wrapping a frozen structure predictor's positional encoding into a ring.

Protein language model that annotates intrinsically disordered regions per residue from sequence alone, without MSAs or biophysical features.

De novo protein backbone generation by SE(3) diffusion over rigid-body frames. Samples designable monomers up to 500 residues without pretraining.

Geometric relational graph neural network that encodes 3D protein structures through geometry-aware message passing and self-supervised pretraining.