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  Machine learning implicit solvation for molecular dynamics

Chen, Y., Krämer, A., Charron, N. E., Husic, B. E., Clementi, C., & Noé, F. (2021). Machine learning implicit solvation for molecular dynamics. The Journal of Chemical Physics, 155(8): 084101. doi:10.1063/5.0059915.

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J Chem Phys_Chen et al_2021.pdf (Publisher version), 8MB
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© 2021 Author(s). Published under an exclusive license by AIP Publishing.
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 Creators:
Chen, Yaoyi1, Author                 
Krämer, Andreas, Author
Charron, Nicholas E. , Author
Husic, Brooke E. , Author
Clementi, Cecilia , Author
Noé, Frank, Author
Affiliations:
1IMPRS for Biology and Computation (Anne-Dominique Gindrat), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479666              

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 Abstract: Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scales for large system sizes is to incorporate the effect of the solvent in a mean-field fashion with implicit solvent models. However, a challenge with existing implicit solvent models is that they often lack accuracy or certain physical properties compared to explicit solvent models as the many-body effects of the neglected solvent molecules are difficult to model as a mean field. Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data. Following the previous ML-CG models CGnet and CGSchnet, we introduce ISSNet, a graph neural network, to model the implicit solvent potential of mean force. ISSNet can learn from explicit solvent simulation data and be readily applied to molecular dynamics simulations. We compare the solute conformational distributions under different solvation treatments for two peptide systems. The results indicate that ISSNet models can outperform widely used generalized Born and surface area models in reproducing the thermodynamics of small protein systems with respect to explicit solvent. The success of this novel method demonstrates the potential benefit of applying machine learning methods in accurate modeling of solvent effects for in silico research and biomedical applications.

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 Dates: 2021-08-042021-08-25
 Publication Status: Published online
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 Identifiers: DOI: 10.1063/5.0059915
PMID: 34470360
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Title: The Journal of Chemical Physics
  Abbreviation : J. Chem. Phys.
Source Genre: Journal
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Publ. Info: Woodbury, N.Y. : American Institute of Physics
Pages: - Volume / Issue: 155 (8) Sequence Number: 084101 Start / End Page: - Identifier: ISSN: 0021-9606
CoNE: https://pure.mpg.de/cone/journals/resource/954922836226