How can molecular dynamics and machine learning be used?
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Molecular dynamics (MD) simulations can be used to study the dynamical properties of various systems, such as glass dynamics , G-protein coupled receptors (GPCRs) , and ferroelectric perovskites . MD simulations provide insights into the behavior and interactions of atoms and molecules over time, allowing for the investigation of structural changes, binding mechanisms, and phase transitions. Machine learning (ML) techniques can be applied to MD simulations to enhance their predictive capabilities and efficiency. ML-based approaches have been used for binding affinity prediction in drug discovery, incorporating structural dynamic information derived from MD simulations . Additionally, ML methods have been employed to develop interatomic potentials for MD simulations, enabling the study of complex systems with improved accuracy and computational efficiency . Overall, the combination of MD simulations and ML techniques offers a powerful approach for understanding and predicting the behavior of molecular systems.
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| Papers (5) | Insight |
|---|---|
2 Citations | The paper discusses how molecular dynamics simulations can be used to improve predictions of protein-ligand binding affinity when combined with machine learning algorithms. |
| Molecular dynamics and machine learning can be used together to construct interatomic potentials for the simulation of ferroelectric perovskites, as described in the provided paper. | |
19 Dec 2022 | The paper discusses the application of machine learning to computer simulations of molecular dynamics involving electronically excited states. It does not specifically mention how molecular dynamics and machine learning can be used together. |
| The paper discusses the use of molecular dynamics simulations and machine learning-based statistical analyses to study the binding-induced dynamical changes in the β2-adrenergic receptor and adrenaline. | |
| The paper discusses the use of a graph neural network (GNN) framework to simulate complex glass dynamics solely from their static structure, bypassing the need for physics laws. |
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