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 121–130 of 175 papers

TitleStatusHype
Plug-and-Play Controllable Generation for Discrete Masked Models—0
BERT and LLMs-Based avGFP Brightness Prediction and Mutation Design—0
Prediction of amino acid side chain conformation using a deep neural network—0
Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity—0
Training Free Guided Flow Matching with Optimal Control—0
Benchmarking deep generative models for diverse antibody sequence design—0
Backdiff: a diffusion model for generalized transferable protein backmapping—0
Fast uncovering of protein sequence diversity from structure—0
Prot42: a Novel Family of Protein Language Models for Target-aware Protein Binder Generation—0
A Variational Perspective on Generative Protein Fitness Optimization—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