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 151–175 of 175 papers

TitleStatusHype
ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering—0
Protein Structure Prediction until CASP15—0
Persistent Sheaf Laplacian Analysis of Protein FlexibilityCode0
Scalable Coupling of Deep Learning with Logical ReasoningCode0
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein DesignCode0
CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language ModelsCode0
PSBench: a large-scale benchmark for estimating the accuracy of protein complex structural modelsCode0
ProDCoNN-server: a web server for protein sequence prediction and design from a three-dimensional structureCode0
Multi-Objective Quality-Diversity in Unstructured and Unbounded SpacesCode0
mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusionCode0
Robust Model-Based Optimization for Challenging Fitness LandscapesCode0
Scoring-Assisted Generative Exploration for Proteins (SAGE-Prot): A Framework for Multi-Objective Protein Optimization via Iterative Sequence Generation and EvaluationCode0
Generative Models for Graph-Based Protein DesignCode0
Building Confidence in Deep Generative Protein DesignCode0
Generative Adversarial Model-Based Optimization via Source Critic RegularizationCode0
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure PredictionCode0
Fast and Flexible Protein Design Using Deep Graph Neural NetworksCode0
Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and ReviewCode0
Gate-based Quantum Computing for Protein DesignCode0
Variational auto-encoding of protein sequencesCode0
Protein Sequence Design with Batch Bayesian OptimisationCode0
Expected flow networks in stochastic environments and two-player zero-sum gamesCode0
Beyond Human-Like Processing: Large Language Models Perform Equivalently on Forward and Backward Scientific TextCode0
Expanding functional protein sequence space using generative adversarial networksCode0
Conditioning by adaptive sampling for robust designCode0
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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