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 51–75 of 175 papers

TitleStatusHype
Learning from Protein Structure with Geometric Vector PerceptronsCode1
Energy-based models for atomic-resolution protein conformationsCode1
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
Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction—0
AlphaFold Database Debiasing for Robust Inverse Folding—0
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
Protein Design with Dynamic Protein Vocabulary—0
PDFBench: A Benchmark for De novo Protein Design from Function—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
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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