Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media-Reference-Cited by-同舟云学术

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media

Published:2023-12-20 Issue:1 Volume:20 Page:411-420
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Coste Amaury¹,Slejko Ema¹²,Zavadlav Julija³^ORCID,Praprotnik Matej¹²^ORCID

Affiliation:

1. Laboratory for Molecular Modeling, National Institute of Chemistry, Ljubljana SI-1001, Slovenia

2. Department of Physics, Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana SI-1000, Slovenia

3. Professorship of Multiscale Modeling of Fluid Materials, TUM School of Engineering and Design, Technical University of Munich, Garching Near Munich DE-85748, Germany

Funder

Javna Agencija za Raziskovalno Dejavnost RS

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.3c00984

Reference133 articles.

1. RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

2. PNAS Plus: From the Cover: A gating mechanism of pentameric ligand-gated ion channels

3. Conformational Heterogeneity of RNA Stem-Loop Hairpins Bound to FUS-RNA Recognition Motif with Disordered RGG Tail Revealed by Unbiased Molecular Dynamics Simulations

4. Effects of Conformational Constraint on Peptide Solubility Limits

5. Benchmarking Molecular Dynamics Force Fields for All-Atom Simulations of Biological Condensates

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