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 41–50 of 175 papers

TitleStatusHype
Diffusion Models for Constrained DomainsCode1
Improving few-shot learning-based protein engineering with evolutionary samplingCode1
Generative power of a protein language model trained on multiple sequence alignmentsCode1
Diffusion Sequence Models for Enhanced Protein Representation and GenerationCode1
Bridge-IF: Learning Inverse Protein Folding with Markov BridgesCode1
Learning the Language of Protein StructureCode1
Controllable Protein Sequence Generation with LLM Preference OptimizationCode1
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein DesignCode1
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein DesignCode1
Fast non-autoregressive inverse folding with discrete diffusionCode1
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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