All Competitors

Gene Ontology prediction for malaria parasite proteins, trained on SAR supergroup structure embeddings with calibrated uncertainty and FDR control.

Pocket-conditioned 3D ligand generator trained on its own predicted conformations, closing the train-inference gap that degrades diffusion sampling.

Structure-based drug design model that inpaints a 3D ligand density into an empty protein pocket, then decodes those voxels into valid SMILES.

Text-guided protein editing framework with disentangled structure and function latents, edited by rewriting either description at inference time.

Protein model quality assessment predicting per-residue lDDT from a single structure, using ultrafast shape recognition to encode residue topology.

Structure-based drug design diffusion model that re-extracts the essential binding subcomplex from a pocket at every step of 3D ligand generation.

De novo protein backbone design with geometric-algebra attention, sampling designable structures whose secondary structure matches natural proteins.

Protein function captioning model fusing sequence, Foldseek structure tokens, and text through a BLIP-2 Q-Former for open-ended free-text annotation.

De novo peptide design across non-canonical amino acid space, using guided diffusion over receptor-ligand interfaces to reach D-amino acid chemistry.

CATH superfamily classifier over ProstT5 amino-acid and 3Di structural-alphabet embeddings, reaching 92.2% accuracy on roughly 1,700 superfamilies.

Tri-modal protein language model aligning sequence, structure, and text in one embedding space for natural-language search over billions of proteins.

SE(3)-equivariant flow-matching model for pocket-aware 3D ligand generation, predicting binding affinity and confidence in the same network.

Geometric deep learning model generating context-aware protein representations across 156 cell-type contexts from a multi-organ single-cell atlas.

Protein sequence-structure co-design model conditioned on Gene Ontology function embeddings, sampling residues and backbone angles together.

Transformer that predicts protein-RNA binding affinity from Boltz-2 pre-structural embeddings via cross-modal attention, with no 3D structure step.

Structure-conditioned protein language model aligned to experimental stability data, scoring variant stability and generating stabilized sequences.

Antibody CDR design model that reprograms a frozen English BERT for sequence infilling, avoiding training a dedicated protein language model.

Message-passing neural network that designs buried hydrogen-bond networks onto protein backbones, combining learned placement with PyRosetta scoring.

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

Protein function prediction model that fuses sequence, structure, text, and interaction embeddings with learned gating to assign Gene Ontology terms.

Blind protein-ligand docking that transfers to binding domains absent from training, scoring 22.6% top-1 on DockGen and 50% on PoseBusters.

Structure-based virtual screening model that jointly predicts protein-ligand complex structures and binding fitness from sequence and SMILES.

Conditional chemical language model prompted with a protein target and mechanism of action to score and design molecules without structural input.