SOTAVerified

Protein Structure Prediction

Papers

Showing 1–25 of 188 papers

TitleStatusHype
Conformation-Aware Structure Prediction of Antigen-Recognizing Immune ProteinsCode1
MegaFold: System-Level Optimizations for Accelerating Protein Structure Prediction ModelsCode2
Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion—0
Multiscale guidance of AlphaFold3 with heterogeneous cryo-EM data—0
Aligning Protein Conformation Ensemble Generation with Physical Feedback—0
Unfolding AlphaFold's Bayesian Roots in Probability Kinematics—0
Transformers in Protein: A Survey—0
PSBench: a large-scale benchmark for estimating the accuracy of protein complex structural modelsCode0
LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization—0
AutoLoop: a novel autoregressive deep learning method for protein loop prediction with high accuracy—0
Cognitio Emergens: Agency, Dimensions, and Dynamics in Human-AI Knowledge Co-Creation—0
Exploring zero-shot structure-based protein fitness prediction—0
From sequence to protein structure and conformational dynamics with AI/ML—0
AI-guided Antibiotic Discovery Pipeline from Target Selection to Compound Identification—0
PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks—0
Advanced Deep Learning Methods for Protein Structure Prediction and Design—0
Towards Interpretable Protein Structure Prediction with Sparse AutoencodersCode1
Non-Canonical Crosslinks Confound Evolutionary Protein Structure Models—0
Leveraging Sequence Purification for Accurate Prediction of Multiple Conformational States with AlphaFold2—0
A Model-Centric Review of Deep Learning for Protein Design—0
Protein Large Language Models: A Comprehensive SurveyCode2
MotifBench: A standardized protein design benchmark for motif-scaffolding problemsCode2
Deep Learning of Proteins with Local and Global Regions of DisorderCode1
PyMOLfold: Interactive Protein and Ligand Structure Prediction in PyMOLCode2
CENTS: Generating synthetic electricity consumption time series for rare and unseen scenarios—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GAL 125MValidation perplexity20.62—Unverified
2GAL 1.3BValidation perplexity17.58—Unverified
3GAL 6.7BValidation perplexity17.29—Unverified
4GAL 30BValidation perplexity17.27—Unverified
5GAL 120BValidation perplexity17.26—Unverified
#ModelMetricClaimedVerifiedStatus
1GAL 125MValidation perplexity19.18—Unverified
2GAL 1.3BValidation perplexity17.04—Unverified
3GAL 6.7BValidation perplexity16.35—Unverified
4GAL 30BValidation perplexity15.42—Unverified
5GAL 120BValidation perplexity12.77—Unverified
#ModelMetricClaimedVerifiedStatus
1GAL 125MValidation perplexity16.35—Unverified
2GAL 1.3BValidation perplexity12.53—Unverified
3GAL 6.7BValidation perplexity7.76—Unverified
4GAL 30BValidation perplexity4.28—Unverified
5GAL 120BValidation perplexity3.14—Unverified
#ModelMetricClaimedVerifiedStatus
1GAL 125MValidation perplexity19.05—Unverified
2GAL 1.3BValidation perplexity15.82—Unverified
3GAL 6.7BValidation perplexity11.58—Unverified
4GAL 30BValidation perplexity8.23—Unverified
5GAL 120BValidation perplexity5.54—Unverified