Machine Learning Prediction of Hydration Free Energy with Physically Inspired Descriptors-Reference-Cited by-同舟云学术

Machine Learning Prediction of Hydration Free Energy with Physically Inspired Descriptors

Published:2023-02-13 Issue:7 Volume:14 Page:1877-1884
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Zhang Zhan-Yun¹,Peng Ding¹,Liu Lihong¹,Shen Lin¹²^ORCID,Fang Wei-Hai¹³^ORCID

Affiliation:

1. Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, P. R. China

2. Yantai-Jingshi Institute of Material Genome Engineering, Yantai 265505, Shandong, P. R. China

3. Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing, Yantai 264006, Shandong, P. R. China

Funder

Ministry of Science and Technology of the People's Republic of China

Beijing Normal University

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.2c03858

Reference57 articles.

1. On the Nonpolar Hydration Free Energy of Proteins: Surface Area and Continuum Solvent Models for the Solute−Solvent Interaction Energy

2. Water — an enduring mystery

3. Solvation Thermodynamics of Organic Molecules by the Molecular Integral Equation Theory: Approaching Chemical Accuracy

4. Accurate and rapid calculation of hydration free energy and its physical implication for biomolecular functions

5. Accurate Prediction of Aqueous Free Solvation Energies Using 3D Atomic Feature-Based Graph Neural Network with Transfer Learning

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