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

Showing 51–60 of 175 papers

TitleStatusHype
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein DesignCode1
Protein Design with Guided Discrete DiffusionCode1
ProDCoNN-server: a web server for protein sequence prediction and design from a three-dimensional structureCode0
Persistent Sheaf Laplacian Analysis of Protein FlexibilityCode0
Robust Model-Based Optimization for Challenging Fitness LandscapesCode0
Fast and Flexible Protein Design Using Deep Graph Neural NetworksCode0
Expected flow networks in stochastic environments and two-player zero-sum gamesCode0
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure PredictionCode0
Expanding functional protein sequence space using generative adversarial networksCode0
Multi-Objective Quality-Diversity in Unstructured and Unbounded SpacesCode0
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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