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 126–150 of 175 papers

TitleStatusHype
Benchmarking deep generative models for diverse antibody sequence design—0
Backdiff: a diffusion model for generalized transferable protein backmapping—0
Fast uncovering of protein sequence diversity from structure—0
Prot42: a Novel Family of Protein Language Models for Target-aware Protein Binder Generation—0
A Variational Perspective on Generative Protein Fitness Optimization—0
ProtDAT: A Unified Framework for Protein Sequence Design from Any Protein Text Description—0
A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design—0
ProteinBench: A Holistic Evaluation of Protein Foundation Models—0
Protein design by multiobjective optimization: evolutionary and non-evolutionary approaches—0
Protein Design with Dynamic Protein Vocabulary—0
Applying computational protein design to therapeutic antibody discovery -- current state and perspectives—0
ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering—0
UniIF: Unified Molecule Inverse Folding—0
Protein Structure Prediction until CASP15—0
ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design—0
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning—0
ProtFlow: Fast Protein Sequence Design via Flow Matching on Compressed Protein Language Model Embeddings—0
ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation—0
ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models—0
Using GANs for De Novo Protein Design Targeting Microglial IL-3Rα to Inhibit Alzheimer's Progression—0
Recent advances in interpretable machine learning using structure-based protein representations—0
Reinforcement Learning for Sequence Design Leveraging Protein Language Models—0
A natural upper bound to the accuracy of predicting protein stability changes upon mutations—0
A Model-Centric Review of Deep Learning for Protein Design—0
Validation of an LLM-based Multi-Agent Framework for Protein Engineering in Dry Lab and Wet Lab—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