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Protein Structure Prediction

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 188 papers

TitleStatusHype
Liquid-theory analogy of direct-coupling analysis of multiple-sequence alignment and its implications for protein structure prediction—0
Mass spectrometry for semi-experimental protein structure determination and modeling—0
Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction—0
Molecular Dynamics—0
Monte Carlo for Protein Structures—0
MultiMed: Massively Multimodal and Multitask Medical Understanding—0
NMRPy: a novel NMR scripting system to implement artificial intelligence and advanced applications—0
Non-Canonical Crosslinks Confound Evolutionary Protein Structure Models—0
Numeric Lyndon-based feature embedding of sequencing reads for machine learning approaches—0
On How AI Needs to Change to Advance the Science of Drug Discovery—0
On the performance of deep learning for numerical optimization: an application to protein structure prediction—0
PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks—0
Predicting the real-valued distances between residue pairs for proteins—0
Preface to Introduction to Protein Structural Bioinformatics—0
Protein Secondary Structure Prediction Using Deep Multi-scale Convolutional Neural Networks and Next-Step Conditioning—0
Protein sequence-to-structure learning: Is this the end(-to-end revolution)?—0
Protein single-model quality assessment by feature-based probability density functions—0
Protein Structure Prediction by Protein Alignments—0
Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning—0
Protein Structure Prediction until CASP15—0
Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction—0
Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer—0
PSMACA: An Automated Protein Structure Prediction Using MACA (Multiple Attractor Cellular Automata)—0
Ratio law: mathematical descriptions for a universal relationship between AI performance and input samples—0
Real-value and confidence prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning—0
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All datasetsCASPSeqCASPSimSeqPaenSeqUniProtSeq

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