Editorial: Mechanisms, thermodynamics and kinetics of ligand binding revealed from molecular simulations and machine learning

Yinglong Miao; Chia-En A Chang; Weiliang Zhu; J Andrew McCammon

doi:10.3389/fmolb.2023.1139471

Editorial: Mechanisms, thermodynamics and kinetics of ligand binding revealed from molecular simulations and machine learning

Front Mol Biosci. 2023 Jan 17:10:1139471. doi: 10.3389/fmolb.2023.1139471. eCollection 2023.

Authors

Yinglong Miao¹, Chia-En A Chang², Weiliang Zhu³, J Andrew McCammon⁴

Affiliations

¹ Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, United States.
² Department of Chemistry, University of California, Riverside, Riverside, CA, United States.
³ Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
⁴ Departments of Chemistry & Biochemistry and Pharmacology, University of California San Diego, San Diego, San Diego, CA, United States.

No abstract available

Keywords: brownian dynamics; kinetics; ligand binding; machine learning; mechanisms; molecular docking; molecular dynamics; thermodynamics.

Publication types

Editorial

Grants and funding

YM is supported by the National Institutes of Health (R01GM132572) and National Science Foundation (2121063). C-EC is supported by the National Institutes of Health (R01GM109045) and National Science Foundation (MCB1932984). JM is supported by the National Institutes of Health grant GM31749. WZ is supported by the National Science Foundation of China (82273851) and National Key R&D Program (2022YFA1004304).