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Convergence criteria for single-step free-energy calculations : the relation between the Π bias measure and the sample variance

Wang, Meiting LU ; Mei, Ye and Ryde, Ulf LU orcid (2024) In Chemical Science 15(23). p.8786-8799
Abstract

Free energy calculations play a crucial role in simulating chemical processes, enzymatic reactions, and drug design. However, assessing the reliability and convergence of these calculations remains a challenge. This study focuses on single-step free-energy calculations using thermodynamic perturbation. It explores how the sample distributions influence the estimated results and evaluates the reliability of various convergence criteria, including Kofke's bias measure Π and the standard deviation of the energy difference ΔU, σΔU. The findings reveal that for Gaussian distributions, there is a straightforward relationship between Π and σΔU, free energies can be accurately approximated using a second-order cumulant... (More)

Free energy calculations play a crucial role in simulating chemical processes, enzymatic reactions, and drug design. However, assessing the reliability and convergence of these calculations remains a challenge. This study focuses on single-step free-energy calculations using thermodynamic perturbation. It explores how the sample distributions influence the estimated results and evaluates the reliability of various convergence criteria, including Kofke's bias measure Π and the standard deviation of the energy difference ΔU, σΔU. The findings reveal that for Gaussian distributions, there is a straightforward relationship between Π and σΔU, free energies can be accurately approximated using a second-order cumulant expansion, and reliable results are attainable for σΔU up to 25 kcal mol−1. However, interpreting non-Gaussian distributions is more complex. If the distribution is skewed towards more positive values than a Gaussian, converging the free energy becomes easier, rendering standard convergence criteria overly stringent. Conversely, distributions that are skewed towards more negative values than a Gaussian present greater challenges in achieving convergence, making standard criteria unreliable. We propose a practical approach to assess the convergence of estimated free energies.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Chemical Science
volume
15
issue
23
pages
14 pages
publisher
Royal Society of Chemistry
external identifiers
  • pmid:38873060
  • scopus:85193626420
ISSN
2041-6520
DOI
10.1039/d4sc00140k
language
English
LU publication?
yes
id
76982ee5-2483-427d-9922-40c3704f953a
date added to LUP
2024-06-13 15:07:30
date last changed
2025-07-26 05:26:17
@article{76982ee5-2483-427d-9922-40c3704f953a,
  abstract     = {{<p>Free energy calculations play a crucial role in simulating chemical processes, enzymatic reactions, and drug design. However, assessing the reliability and convergence of these calculations remains a challenge. This study focuses on single-step free-energy calculations using thermodynamic perturbation. It explores how the sample distributions influence the estimated results and evaluates the reliability of various convergence criteria, including Kofke's bias measure Π and the standard deviation of the energy difference ΔU, σ<sub>ΔU</sub>. The findings reveal that for Gaussian distributions, there is a straightforward relationship between Π and σ<sub>ΔU</sub>, free energies can be accurately approximated using a second-order cumulant expansion, and reliable results are attainable for σ<sub>ΔU</sub> up to 25 kcal mol<sup>−1</sup>. However, interpreting non-Gaussian distributions is more complex. If the distribution is skewed towards more positive values than a Gaussian, converging the free energy becomes easier, rendering standard convergence criteria overly stringent. Conversely, distributions that are skewed towards more negative values than a Gaussian present greater challenges in achieving convergence, making standard criteria unreliable. We propose a practical approach to assess the convergence of estimated free energies.</p>}},
  author       = {{Wang, Meiting and Mei, Ye and Ryde, Ulf}},
  issn         = {{2041-6520}},
  language     = {{eng}},
  number       = {{23}},
  pages        = {{8786--8799}},
  publisher    = {{Royal Society of Chemistry}},
  series       = {{Chemical Science}},
  title        = {{Convergence criteria for single-step free-energy calculations : the relation between the Π bias measure and the sample variance}},
  url          = {{http://dx.doi.org/10.1039/d4sc00140k}},
  doi          = {{10.1039/d4sc00140k}},
  volume       = {{15}},
  year         = {{2024}},
}