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 51–60 of 175 papers

TitleStatusHype
Practical and Asymptotically Exact Conditional Sampling in Diffusion ModelsCode1
Reinforcement learning on structure-conditioned categorical diffusion for protein inverse foldingCode1
Computational Protein Science in the Era of Large Language Models (LLMs)—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 Protein Design with Deep Learning Neural Networks—0
Generative AI for Controllable Protein Sequence Design: A Survey—0
Computational design of target-specific linear peptide binders with TransformerBeta—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