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 1–50 of 175 papers

TitleStatusHype
Toward the Explainability of Protein Language Models for Sequence Design—0
Geometric deep learning assists protein engineering. Opportunities and Challenges—0
Natural Language Guided Ligand-Binding Protein Design—0
AlphaFold Database Debiasing for Robust Inverse Folding—0
Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction—0
Diffusion Sequence Models for Enhanced Protein Representation and GenerationCode1
Improving large language models with concept-aware fine-tuningCode1
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning—0
Improving Protein Sequence Design through Designability Preference Optimization—0
CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language ModelsCode0
Breaking the Performance Ceiling in Complex Reinforcement Learning requires Inference Strategies—0
PDFBench: A Benchmark for De novo Protein Design from Function—0
Protein Design with Dynamic Protein Vocabulary—0
DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design—0
PSBench: a large-scale benchmark for estimating the accuracy of protein complex structural modelsCode0
Scoring-Assisted Generative Exploration for Proteins (SAGE-Prot): A Framework for Multi-Objective Protein Optimization via Iterative Sequence Generation and EvaluationCode0
ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation—0
Sparks: Multi-Agent Artificial Intelligence Model Discovers Protein Design Principles—0
The Dance of Atoms-De Novo Protein Design with Diffusion Model—0
ProtFlow: Fast Protein Sequence Design via Flow Matching on Compressed Protein Language Model Embeddings—0
Prot42: a Novel Family of Protein Language Models for Target-aware Protein Binder Generation—0
Multi-Objective Quality-Diversity in Unstructured and Unbounded SpacesCode0
Advanced Deep Learning Methods for Protein Structure Prediction and Design—0
ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models—0
Applying computational protein design to therapeutic antibody discovery -- current state and perspectives—0
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