Research Topic

Machine Learning in Biomolecular Simulations

About this Research Topic

This Research Topic collection will focus on the application of machine learning algorithms in biomolecular simulations. In particular, it will cover the application of:

- advanced non-linear dimensionality reduction techniques
- advanced clustering methods
- supervised machine learning methods such as support vector machines or decision trees
- genetic algorithms
- (deep) neural networks and autoencoders
- reinforcement learning
- big data approaches
- other related techniques

We are interested in original manuscripts as well as expert reviews on the application of these techniques in:

- clustering and dimensionality reduction of molecular structure, especially in the analysis of simulation trajectories motivated by free energy modeling
- approximation of molecular potential by machine learning algorithms
- machine learning for the building of thermodynamic and kinetic models of molecular systems
- application of machine learning in sampling enhancement
- machine learning in multi-scale modelling
- machine learning to link molecular simulations with experiments
- software tools for application of machine learning in molecular simulations

Please contact the topic editors with a short description of the study or topic covered by the planned manuscript, or submit an abstract via the portal above prior to full submission.


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

This Research Topic collection will focus on the application of machine learning algorithms in biomolecular simulations. In particular, it will cover the application of:

- advanced non-linear dimensionality reduction techniques
- advanced clustering methods
- supervised machine learning methods such as support vector machines or decision trees
- genetic algorithms
- (deep) neural networks and autoencoders
- reinforcement learning
- big data approaches
- other related techniques

We are interested in original manuscripts as well as expert reviews on the application of these techniques in:

- clustering and dimensionality reduction of molecular structure, especially in the analysis of simulation trajectories motivated by free energy modeling
- approximation of molecular potential by machine learning algorithms
- machine learning for the building of thermodynamic and kinetic models of molecular systems
- application of machine learning in sampling enhancement
- machine learning in multi-scale modelling
- machine learning to link molecular simulations with experiments
- software tools for application of machine learning in molecular simulations

Please contact the topic editors with a short description of the study or topic covered by the planned manuscript, or submit an abstract via the portal above prior to full submission.


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Topic Editors

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Submission Deadlines

01 March 2019 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

01 March 2019 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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