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 91–100 of 175 papers

TitleStatusHype
Learning immune receptor representations with protein language models—0
Conditional Generative Modeling for De Novo Hierarchical Multi-Label Functional Protein Design—0
Leveraging Deep Generative Model For Computational Protein Design And Optimization—0
CCPL: Cross-modal Contrastive Protein Learning—0
MADE: Graph Backdoor Defense with Masked Unlearning—0
MAP Estimation for Graphical Models by Likelihood Maximization—0
MeMDLM: De Novo Membrane Protein Design with Masked Discrete Diffusion Protein Language Models—0
Computational Protein Science in the Era of Large Language Models (LLMs)—0
Metropolis Sampling for Constrained Diffusion Models—0
The Dance of Atoms-De Novo Protein Design with Diffusion Model—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