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 81–90 of 175 papers

TitleStatusHype
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
Show:102550
← PrevPage 9 of 18Next →
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