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

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Showing 151–160 of 175 papers

TitleStatusHype
Efficient generative modeling of protein sequences using simple autoregressive models—0
Mimetic Neural Networks: A unified framework for Protein Design and Folding—0
Conditional Generative Modeling for De Novo Hierarchical Multi-Label Functional Protein Design—0
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning—0
Fast and Flexible Protein Design Using Deep Graph Neural NetworksCode0
Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity—0
Lattice protein design using Bayesian learning—0
Generative Models for Graph-Based Protein DesignCode0
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