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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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
A five-level physics-informed hierarchical GNN with bidirectional cross-scale fusion substantially improves hard fold classification and reaction-class prediction over geometric and sequence baselines.
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.
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.
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.
citing papers explorer
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EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning
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.
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Structural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning
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
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PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies
A five-level physics-informed hierarchical GNN with bidirectional cross-scale fusion substantially improves hard fold classification and reaction-class prediction over geometric and sequence baselines.
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Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction
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.
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CryoProt: A Protein Pretraining Framework with Cross-Box Interactions on Cryo-EM Density Maps
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.
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SurfDesign: Effective Protein Design on Molecular Surfaces
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.
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EvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation
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%.
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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.
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Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach
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.
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BioBlobs: Unsupervised Discovery of Functional Substructures for Protein Function 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.
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STELLA: A Multimodal LLM for Protein Functional Annotation via Unified Sequence-Structure Encoding
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.
- AgForce Enables Antigen-conditioned Generative Antibody Design
- ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning