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Protein Folding

Papers

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Showing 101–125 of 185 papers

TitleStatusHype
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules—0
ParaFold: Paralleling AlphaFold for Large-Scale PredictionsCode1
RoMA: a Method for Neural Network Robustness Measurement and Assessment—0
Protein Folding Neural Networks Are Not Robust—0
Variational embedding of protein folding simulations using gaussian mixture variational autoencoders—0
Highly accurate protein structure prediction with AlphaFoldCode3
Exploring generative atomic models in cryo-EM reconstruction—0
Embedding Principle of Loss Landscape of Deep Neural Networks—0
Protein folding simulations in the hydrophobic-polar model using a hybrid cuckoo search algorithm—0
EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models—0
Predictive landscapes hidden beneath biological cellular automata—0
Quadratic Optimization-Based Nonlinear Control for Protein Conformation Prediction—0
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and GaugesCode2
Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations—0
Rethinking Neural Operations for Diverse TasksCode1
Mimetic Neural Networks: A unified framework for Protein Design and Folding—0
QFold: Quantum Walks and Deep Learning to Solve Protein FoldingCode0
TorchMD: A deep learning framework for molecular simulationsCode1
Analyzing the Performance of Graph Neural Networks with Pipe Parallelism—0
Exact Reduction of Huge Action Spaces in General Reinforcement Learning—0
The finite size effects and the two-state paradigm of protein folding—0
Disentangled Dynamic Graph Deep GenerationCode0
The role of hydrophobic interactions in folding of β-sheets—0
Protein Folding/Unfolding Phenomenon is originated by Synchronization/Desynchronization of Instantaneously Induced Oscillating Dipoles. A Hypothesis—0
Unsupervised and Supervised Structure Learning for Protein Contact Prediction—0
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