SOTAVerified

Protein Structure Prediction

Papers

Showing 125 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 Diffusion0
Multiscale guidance of AlphaFold3 with heterogeneous cryo-EM data0
Aligning Protein Conformation Ensemble Generation with Physical Feedback0
Unfolding AlphaFold's Bayesian Roots in Probability Kinematics0
Transformers in Protein: A Survey0
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 Quantization0
AutoLoop: a novel autoregressive deep learning method for protein loop prediction with high accuracy0
Cognitio Emergens: Agency, Dimensions, and Dynamics in Human-AI Knowledge Co-Creation0
Exploring zero-shot structure-based protein fitness prediction0
From sequence to protein structure and conformational dynamics with AI/ML0
AI-guided Antibiotic Discovery Pipeline from Target Selection to Compound Identification0
PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks0
Advanced Deep Learning Methods for Protein Structure Prediction and Design0
Towards Interpretable Protein Structure Prediction with Sparse AutoencodersCode1
Non-Canonical Crosslinks Confound Evolutionary Protein Structure Models0
Leveraging Sequence Purification for Accurate Prediction of Multiple Conformational States with AlphaFold20
A Model-Centric Review of Deep Learning for Protein Design0
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 scenarios0
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Benchmark Results

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