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 101–110 of 175 papers

TitleStatusHype
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
Fast and Accurate Antibody Sequence Design via Structure Retrieval—0
Fast fixed-backbone protein sequence and rotamer design—0
Folding and Stabilization of Native-Sequence-Reversed Proteins—0
From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering—0
Annotation-guided Protein Design with Multi-Level Domain Alignment—0
Generative AI for Controllable Protein Sequence Design: A Survey—0
Generative artificial intelligence for de novo protein design—0
Generative modeling for protein structures—0
Geometric deep learning assists protein engineering. Opportunities and Challenges—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