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

Showing 4150 of 175 papers

TitleStatusHype
Diffusion Models for Constrained DomainsCode1
Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue CloudsCode1
RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA DesignCode1
AlphaFold Distillation for Protein DesignCode1
PiFold: Toward effective and efficient protein inverse foldingCode1
Generative De Novo Protein Design with Global ContextCode1
Generative power of a protein language model trained on multiple sequence alignmentsCode1
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDBCode1
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-designCode1
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein DesignCode1
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Benchmark Results

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