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 121–130 of 175 papers

TitleStatusHype
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning—0
ProtFlow: Fast Protein Sequence Design via Flow Matching on Compressed Protein Language Model Embeddings—0
ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation—0
ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models—0
Recent advances in interpretable machine learning using structure-based protein representations—0
Reinforcement Learning for Sequence Design Leveraging Protein Language Models—0
Rigidity strengthening is a vital mechanism for protein-ligand binding—0
RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching—0
Sparks: Multi-Agent Artificial Intelligence Model Discovers Protein Design Principles—0
Steering Protein Family Design through Profile Bayesian Flow—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