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 26–50 of 175 papers

TitleStatusHype
Proteina: Scaling Flow-based Protein Structure Generative ModelsCode3
A Model-Centric Review of Deep Learning for Protein Design—0
ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone GenerationCode2
MotifBench: A standardized protein design benchmark for motif-scaffolding problemsCode2
Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model—0
Persistent Sheaf Laplacian Analysis of Protein FlexibilityCode0
Fast and Accurate Antibody Sequence Design via Structure Retrieval—0
Steering Protein Family Design through Profile Bayesian Flow—0
Iterative Importance Fine-tuning of Diffusion Models—0
A Variational Perspective on Generative Protein Fitness Optimization—0
Controllable Protein Sequence Generation with LLM Preference OptimizationCode1
Computational Protein Science in the Era of Large Language Models (LLMs)—0
Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and ReviewCode0
A General Framework for Inference-time Scaling and Steering of Diffusion ModelsCode3
From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering—0
A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design—0
Multi-Attribute Constraint Satisfaction via Language Model Rewriting—0
Open-Source Protein Language Models for Function Prediction and Protein Design—0
ProtDAT: A Unified Framework for Protein Sequence Design from Any Protein Text Description—0
Building Confidence in Deep Generative Protein DesignCode0
MADE: Graph Backdoor Defense with Masked Unlearning—0
Pan-protein Design Learning Enables Task-adaptive Generalization for Low-resource Enzyme Design—0
Beyond Human-Like Processing: Large Language Models Perform Equivalently on Forward and Backward Scientific TextCode0
Validation of an LLM-based Multi-Agent Framework for Protein Engineering in Dry Lab and Wet Lab—0
Concept Bottleneck Language Models For protein designCode2
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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