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

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Showing 31–40 of 175 papers

TitleStatusHype
Bridge-IF: Learning Inverse Protein Folding with Markov BridgesCode1
Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue CloudsCode1
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein DesignCode1
AlphaFold Distillation for Protein DesignCode1
Progressive Multi-Modality Learning for Inverse Protein FoldingCode1
Fast non-autoregressive inverse folding with discrete diffusionCode1
De novo protein design using geometric vector field networksCode1
Learning from Protein Structure with Geometric Vector PerceptronsCode1
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDBCode1
Learning the Language of Protein StructureCode1
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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