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 1–10 of 175 papers

TitleStatusHype
Toward the Explainability of Protein Language Models for Sequence Design—0
Geometric deep learning assists protein engineering. Opportunities and Challenges—0
Natural Language Guided Ligand-Binding Protein Design—0
Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction—0
AlphaFold Database Debiasing for Robust Inverse Folding—0
Diffusion Sequence Models for Enhanced Protein Representation and GenerationCode1
Improving large language models with concept-aware fine-tuningCode1
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning—0
Improving Protein Sequence Design through Designability Preference Optimization—0
CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language ModelsCode0
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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