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Protein Folding

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Showing 51–75 of 185 papers

TitleStatusHype
Endothelial Cell-specific Loss of Breast Cancer Susceptibility Gene 2 Exacerbates Atherosclerosis—0
End-to-End Optimized Pipeline for Prediction of Protein Folding Kinetics—0
A method for partitioning the information contained in a protein sequence between its structure and function—0
Evidence of evolutionary selection for co-translational folding—0
Exact Reduction of Huge Action Spaces in General Reinforcement Learning—0
Exploring generative atomic models in cryo-EM reconstruction—0
FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames—0
First-Passage Time Distributions in Two-State Protein Folding Kinetics: Exploring the Native-Like States vs Overcoming the Free Energy Barrier—0
First-passage times in complex energy landscapes: a case study with nonmuscle myosin II assembly—0
Fluorescence Phasor Analysis: Basic Principles and Biophysical Applications—0
Folding and Stabilization of Native-Sequence-Reversed Proteins—0
Folding@home: achievements from over twenty years of citizen science herald the exascale era—0
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules—0
Doing the impossible: Why neural networks can be trained at all—0
A Tale of Two Desolvation Potentials: An Investigation of Protein Behavior Under High Hydrostatic Pressure—0
Disentangled Spatiotemporal Graph Generative Models—0
A maximum-caliber approach to predicting perturbed folding kinetics due to mutations—0
Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations—0
Hallucination, reliability, and the role of generative AI in science—0
Dimensionality reduction methods for molecular simulations—0
Dense Sample Deep Learning—0
AbODE: Ab Initio Antibody Design using Conjoined ODEs—0
Genetic prediction of quantitative traits: a machine learner's guide focused on height—0
Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction—0
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics—0
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