SOTAVerified

Protein Design

Formally, given the design requirements of users, models are required to generate protein amino acid sequences that align with those requirements.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 175 papers

TitleStatusHype
Learning the Language of Protein StructureCode1
RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA DesignCode1
Folding and Stabilization of Native-Sequence-Reversed Proteins—0
Computational Protein Science in the Era of Large Language Models (LLMs)—0
Computational Protein Design with Deep Learning Neural Networks—0
A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design—0
Fast fixed-backbone protein sequence and rotamer design—0
Computational Protein Design Using AND/OR Branch-and-Bound Search—0
Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model—0
Computational design of target-specific linear peptide binders with TransformerBeta—0
Fast and Accurate Antibody Sequence Design via Structure Retrieval—0
From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering—0
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient—0
Applying computational protein design to therapeutic antibody discovery -- current state and perspectives—0
Clusters and Coarse Partitions in LP Relaxations—0
Ideal gas behavior of rotamerically defined conformers in native globular proteins—0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
A natural upper bound to the accuracy of predicting protein stability changes upon mutations—0
EMOCPD: Efficient Attention-based Models for Computational Protein Design Using Amino Acid Microenvironment—0
Efficient generative modeling of protein sequences using simple autoregressive models—0
Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction—0
Improving Protein Sequence Design through Designability Preference Optimization—0
DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design—0
DockGame: Cooperative Games for Multimeric Rigid Protein Docking—0
Diffusion on language model encodings for protein sequence generation—0
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All datasetsCATH 4.2CATH 4.3

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GraphTransPerplexity6.63—Unverified
2StructGNNPerplexity6.4—Unverified
3AlphaDesignPerplexity6.3—Unverified
4GCAPerplexity6.05—Unverified
5GVPPerplexity5.36—Unverified
6ProteinMPNNPerplexity4.61—Unverified
7PiFoldPerplexity4.55—Unverified
8Knowledge-DesignPerplexity3.46—Unverified
#ModelMetricClaimedVerifiedStatus
1ESM-IFPerplexity6.44—Unverified
2GVP-largePerplexity6.17—Unverified