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 101–125 of 175 papers

TitleStatusHype
Mimetic Neural Networks: A unified framework for Protein Design and Folding—0
Computational Protein Design with Deep Learning Neural Networks—0
Model-based reinforcement learning for protein backbone design—0
Modular decomposition of protein structure using community detection—0
Computational Protein Design Using AND/OR Branch-and-Bound Search—0
Computational design of target-specific linear peptide binders with TransformerBeta—0
Multi-Attribute Constraint Satisfaction via Language Model Rewriting—0
Controllable Protein Design with Language Models—0
Natural Language Guided Ligand-Binding Protein Design—0
Clusters and Coarse Partitions in LP Relaxations—0
Open-Source Protein Language Models for Function Prediction and Protein Design—0
Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design—0
Breaking the Performance Ceiling in Complex Reinforcement Learning requires Inference Strategies—0
Pan-protein Design Learning Enables Task-adaptive Generalization for Low-resource Enzyme Design—0
What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs—0
PDBench: Evaluating Computational Methods for Protein Sequence Design—0
Towards deep learning sequence-structure co-generation for protein design—0
PDFBench: A Benchmark for De novo Protein Design from Function—0
Boosting AND/OR-Based Computational Protein Design: Dynamic Heuristics and Generalizable UFO—0
Toward the Explainability of Protein Language Models for Sequence Design—0
Plug-and-Play Controllable Generation for Discrete Masked Models—0
BERT and LLMs-Based avGFP Brightness Prediction and Mutation Design—0
Prediction of amino acid side chain conformation using a deep neural network—0
Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity—0
Training Free Guided Flow Matching with Optimal Control—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