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Research Frontiers in Physics-Informed Neural Networks Protein Dynamics

Incorporating physical constraints into neural networks to predict protein folding dynamics and conformational changes.

Physics-Constrained Latent Spaces for Protein Folding Trajectories
Hamiltonian Neural Networks in Biomolecular Conformational Sampling
Symmetry-Preserving Architectures for Enzyme Catalytic Mechanisms
Energy Landscape Reconstruction via Differentiable Molecular Dynamics
Conservation Laws as Inductive Biases in Protein Dynamics Models
Neural Operators for Multiscale Biomolecular Motion Prediction
Variational Inference of Hidden Protein Dynamics from Sparse Data
Physics-Aware Graph Neural Networks for Allosteric Protein Communication
Thermodynamic Consistency in Deep Learning Protein Interactions
Learnable Physical Constraints for De Novo Protein Structure Dynamics

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