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
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
Learning from Protein Structure with Geometric Vector PerceptronsCode1
Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity—0
Energy-based models for atomic-resolution protein conformationsCode1
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
Generative modeling for protein structures—0
Void distributions reveal structural link between jammed packings and protein cores—0
A natural upper bound to the accuracy of predicting protein stability changes upon mutations—0
Modular decomposition of protein structure using community detection—0
Computational Protein Design with Deep Learning Neural Networks—0
mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusionCode0
Variational auto-encoding of protein sequencesCode0
Prediction of amino acid side chain conformation using a deep neural network—0
Protein design by multiobjective optimization: evolutionary and non-evolutionary approaches—0
Rigidity strengthening is a vital mechanism for protein-ligand binding—0
Folding and Stabilization of Native-Sequence-Reversed Proteins—0
Ideal gas behavior of rotamerically defined conformers in native globular proteins—0
Computational Protein Design Using AND/OR Branch-and-Bound Search—0
MAP Estimation for Graphical Models by Likelihood Maximization—0
Clusters and Coarse Partitions in LP Relaxations—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