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
RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow MatchingCode2
UniIF: Unified Molecule Inverse Folding—0
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient—0
Learning the Language of Protein StructureCode1
Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2Code2
SurfPro: Functional Protein Design Based on Continuous Surface—0
Model-based reinforcement learning for protein backbone design—0
ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering—0
Annotation-guided Protein Design with Multi-Level Domain Alignment—0
Using GANs for De Novo Protein Design Targeting Microglial IL-3Rα to Inhibit Alzheimer's Progression—0
Diffusion on language model encodings for protein sequence generation—0
TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence GenerationCode3
Generative AI for Controllable Protein Sequence Design: A Survey—0
X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Molecular DesignCode3
Generative Adversarial Model-Based Optimization via Source Critic RegularizationCode0
Learning immune receptor representations with protein language models—0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learningCode1
Improved motif-scaffolding with SE(3) flow matchingCode3
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design—0
Progressive Multi-Modality Learning for Inverse Protein FoldingCode1
Fast non-autoregressive inverse folding with discrete diffusionCode1
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein DesignCode0
AI-predicted protein deformation encodes energy landscape—0
De novo protein design using geometric vector field networksCode1
Show:102550
← PrevPage 4 of 7Next →
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