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

Showing 61–70 of 175 papers

TitleStatusHype
CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language ModelsCode0
Building Confidence in Deep Generative Protein DesignCode0
Persistent Sheaf Laplacian Analysis of Protein FlexibilityCode0
ProDCoNN-server: a web server for protein sequence prediction and design from a three-dimensional structureCode0
Multi-Objective Quality-Diversity in Unstructured and Unbounded SpacesCode0
Beyond Human-Like Processing: Large Language Models Perform Equivalently on Forward and Backward Scientific TextCode0
mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusionCode0
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein DesignCode0
Generative Adversarial Model-Based Optimization via Source Critic RegularizationCode0
Conditioning by adaptive sampling for robust designCode0
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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