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 76–100 of 175 papers

TitleStatusHype
Efficient generative modeling of protein sequences using simple autoregressive models—0
DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design—0
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces—0
DockGame: Cooperative Games for Multimeric Rigid Protein Docking—0
Ideal gas behavior of rotamerically defined conformers in native globular proteins—0
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient—0
Diffusion on language model encodings for protein sequence generation—0
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action—0
Design in the Dark: Learning Deep Generative Models for De Novo Protein Design—0
Improving Protein Sequence Design through Designability Preference Optimization—0
Iterative Importance Fine-tuning of Diffusion Models—0
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning—0
De novo design of high-affinity protein binders with AlphaProteo—0
Lattice protein design using Bayesian learning—0
Deep Generative Modeling for Protein Design—0
Learning immune receptor representations with protein language models—0
Conditional Generative Modeling for De Novo Hierarchical Multi-Label Functional Protein Design—0
Leveraging Deep Generative Model For Computational Protein Design And Optimization—0
CCPL: Cross-modal Contrastive Protein Learning—0
MADE: Graph Backdoor Defense with Masked Unlearning—0
MAP Estimation for Graphical Models by Likelihood Maximization—0
MeMDLM: De Novo Membrane Protein Design with Masked Discrete Diffusion Protein Language Models—0
Computational Protein Science in the Era of Large Language Models (LLMs)—0
Metropolis Sampling for Constrained Diffusion Models—0
The Dance of Atoms-De Novo Protein Design with Diffusion Model—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