All Competitors

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.

All-atom protein diffusion model that co-designs backbone, sequence and sidechains by denoising a superposition over all 20 sidechain states.

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.

Invariant point attention network that segments a protein structure into domains, clustering residues bottom-up on experimental and AlphaFold models.

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.

Antibody language model pretrained on 18M OAS Fv sequences with a 70% CDR-H3-targeted mask, giving embeddings for pairing and binding prediction.

MHC class II peptide presentation model that scores epitopes over AlphaFold2-derived residue contact graphs and rates anti-drug antibody risk.

Protein structure accuracy estimation predicting a global TM-score from an equivariant graph network over residue geometry and Rosetta energy terms.