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

Showing 2130 of 175 papers

TitleStatusHype
ProGen2: Exploring the Boundaries of Protein Language ModelsCode2
RITA: a Study on Scaling Up Generative Protein Sequence ModelsCode2
Improving large language models with concept-aware fine-tuningCode1
Diffusion Sequence Models for Enhanced Protein Representation and GenerationCode1
Controllable Protein Sequence Generation with LLM Preference OptimizationCode1
Bridge-IF: Learning Inverse Protein Folding with Markov BridgesCode1
Peptide-GPT: Generative Design of Peptides using Generative Pre-trained Transformers and Bio-informatic SupervisionCode1
Reinforcement learning on structure-conditioned categorical diffusion for protein inverse foldingCode1
Geometric Trajectory Diffusion ModelsCode1
Metalic: Meta-Learning In-Context with Protein Language ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GraphTransPerplexity6.63Unverified
2StructGNNPerplexity6.4Unverified
3AlphaDesignPerplexity6.3Unverified
4GCAPerplexity6.05Unverified
5GVPPerplexity5.36Unverified
6ProteinMPNNPerplexity4.61Unverified
7PiFoldPerplexity4.55Unverified
8Knowledge-DesignPerplexity3.46Unverified
#ModelMetricClaimedVerifiedStatus
1ESM-IFPerplexity6.44Unverified
2GVP-largePerplexity6.17Unverified