Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential-Reference-Cited by-同舟云学术

Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential

Published:2024-07-08 Issue:28 Volume:128 Page:6693-6703
ISSN:1520-6106
Container-title:The Journal of Physical Chemistry B
language:en
Short-container-title:J. Phys. Chem. B

Author:

Karwounopoulos Johannes¹²,Wu Zhiyi³^ORCID,Tkaczyk Sara⁴⁵,Wang Shuzhe³,Baskerville Adam³,Ranasinghe Kavindri³,Langer Thierry⁴^ORCID,Wood Geoffrey P. F.³,Wieder Marcus³⁶,Boresch Stefan¹^ORCID

Affiliation:

1. Faculty of Chemistry, Institute of Computational Biological Chemistry, University Vienna, Währingerstr. 17, 1090 Vienna, Austria

2. Vienna Doctoral School of Chemistry (DoSChem), University of Vienna, Währingerstr. 42, 1090 Vienna, Austria

3. Exscientia plc, Schroedinger Building, Oxford OX4 4GE, United Kingdom

4. Department of Pharmaceutical Sciences, Pharmaceutical Chemistry Division, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria

5. Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences (PhaNuSpo),University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria

6. Open Molecular Software Foundation, Davis, California 95616, United States

Funder

National Institutes of Health

U.S. Department of Health and Human Services

Sloan Kettering Institute

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcb.4c01417

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5. Small Molecule Hydration Free Energies in Explicit Solvent: An Extensive Test of Fixed-Charge Atomistic Simulations