EpiFormer improves epitope prediction F1 score by over 40% via early-fusion cross-attention in GNN layers and sparsity-aware objectives, while recovering known biology as emergent behavior.
arXiv preprint arXiv:2203.06125 , year=
13 Pith papers cite this work. Polarity classification is still indexing.
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ConTact introduces a contact-then-act architecture with distance-biased cross-attention and contact-weighted loss for antibody CDR design, reporting 5-6% better backbone RMSD and superior contact metrics on CHIMERA-Bench splits.
SoftBlobGIN combines ESM-2 representations with protein contact graphs via a lightweight GNN and differentiable substructure pooling to achieve 92.8% accuracy on enzyme classification, raise binding-site AUROC to 0.983, and generate auditable structural explanations without retraining the language模型
SurfBind applies a Transformer with patch-level surface modeling and binder-aware cross-attention to 3D molecular surfaces, reporting state-of-the-art epitope prediction on SAbDab and DB5.5 with generalization to unseen antibodies.
CryoProt pretrains generalizable protein representations from cryo-EM density maps by modeling cross-box interactions with latent attention and multi-task learning, outperforming baselines on downstream tasks.
SurfDesign introduces surface-conditioned protein design via manifold modeling and equivariant message passing on surfaces integrated with pretrained language models, outperforming prior methods on binder and enzyme design benchmarks.
AgForce improves antigen-conditioned antibody design by using framework dropout, gated bottlenecks, hyperbolic cross attention, MDN sequence head with Potts-like coupling, annealed MCL, and antigen cycle consistency to achieve 8% better amino acid recovery and superior binding metrics on CHIMERA-BEN
EvoStruct integrates evolutionary priors from a protein language model with structural priors from an E(3)-equivariant GNN to raise amino acid recovery by 16% and diversity by 2.3x on CHIMERA-Bench while cutting perplexity 43%.
Yeti is a compact tokenizer for protein structures that delivers strong codebook use, token diversity, and reconstruction while enabling from-scratch multimodal generation of plausible sequences and structures with 10x fewer parameters than ESM3.
L3-PPI reformulates PPI pair classification as graph classification over a prompt graph with controlled virtual L3 paths to inject the biological interaction prior and boost performance on existing models.
PRIME is a five-level hierarchical equivariant graph model for proteins that uses physics-informed deterministic operators to exchange information across scales and achieves state-of-the-art results on fold classification and reaction class prediction.
BioBlobs compresses proteins into a small set of cohesive substructures and predicts function from these blobs alone, recovering catalytic sites from protein-level labels across multiple encoders.
STELLA aligns ESM3 bimodal sequence-structure encodings with Llama-3.1-8B text modeling to claim state-of-the-art results on protein functional description prediction and enzyme-catalyzed reaction prediction.
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Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation
Yeti is a compact tokenizer for protein structures that delivers strong codebook use, token diversity, and reconstruction while enabling from-scratch multimodal generation of plausible sequences and structures with 10x fewer parameters than ESM3.