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 31–40 of 175 papers

TitleStatusHype
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein DesignCode1
Learning the Language of Protein StructureCode1
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learningCode1
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
Score-Based Generative Models for Designing Binding Peptide BackbonesCode1
Practical and Asymptotically Exact Conditional Sampling in Diffusion ModelsCode1
Protein Design with Guided Discrete DiffusionCode1
Improving few-shot learning-based protein engineering with evolutionary samplingCode1
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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