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
Reinforcement Learning for Sequence Design Leveraging Protein Language Models—0
Fast uncovering of protein sequence diversity from structure—0
UniIF: Unified Molecule Inverse Folding—0
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient—0
SurfPro: Functional Protein Design Based on Continuous Surface—0
Model-based reinforcement learning for protein backbone design—0
ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering—0
Annotation-guided Protein Design with Multi-Level Domain Alignment—0
Using GANs for De Novo Protein Design Targeting Microglial IL-3Rα to Inhibit Alzheimer's Progression—0
Diffusion on language model encodings for protein sequence generation—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