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 151–175 of 175 papers

TitleStatusHype
Rigidity strengthening is a vital mechanism for protein-ligand binding—0
AI-predicted protein deformation encodes energy landscape—0
RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching—0
Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model—0
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design—0
AlphaFold Database Debiasing for Robust Inverse Folding—0
Advanced Deep Learning Methods for Protein Structure Prediction and Design—0
Void distributions reveal structural link between jammed packings and protein cores—0
Sparks: Multi-Agent Artificial Intelligence Model Discovers Protein Design Principles—0
Steering Protein Family Design through Profile Bayesian Flow—0
SurfPro: Functional Protein Design Based on Continuous Surface—0
Folding and Stabilization of Native-Sequence-Reversed Proteins—0
From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering—0
Annotation-guided Protein Design with Multi-Level Domain Alignment—0
Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction—0
Fast fixed-backbone protein sequence and rotamer design—0
Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design—0
Generative AI for Controllable Protein Sequence Design: A Survey—0
Generative artificial intelligence for de novo protein design—0
Fast and Accurate Antibody Sequence Design via Structure Retrieval—0
Generative modeling for protein structures—0
TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs—0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
EMOCPD: Efficient Attention-based Models for Computational Protein Design Using Amino Acid Microenvironment—0
Geometric deep learning assists protein engineering. Opportunities and Challenges—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