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 91–100 of 175 papers

TitleStatusHype
Deep Generative Modeling for Protein Design—0
De novo design of high-affinity protein binders with AlphaProteo—0
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning—0
Design in the Dark: Learning Deep Generative Models for De Novo Protein Design—0
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action—0
Diffusion on language model encodings for protein sequence generation—0
DockGame: Cooperative Games for Multimeric Rigid Protein Docking—0
DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design—0
Efficient generative modeling of protein sequences using simple autoregressive models—0
EMOCPD: Efficient Attention-based Models for Computational Protein Design Using Amino Acid Microenvironment—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