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 111–120 of 175 papers

TitleStatusHype
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces—0
Ideal gas behavior of rotamerically defined conformers in native globular proteins—0
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient—0
Improving Protein Sequence Design through Designability Preference Optimization—0
Iterative Importance Fine-tuning of Diffusion Models—0
Lattice protein design using Bayesian learning—0
Learning immune receptor representations with protein language models—0
Leveraging Deep Generative Model For Computational Protein Design And Optimization—0
CCPL: Cross-modal Contrastive Protein Learning—0
ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design—0
Show:102550
← PrevPage 12 of 18Next →
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