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
(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.
(Less)
- author
- Wang, Meiting
LU
; Mei, Ye
and Ryde, Ulf
LU
- organization
- publishing date
- 2024
- 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
-
- scopus:85193626420
- pmid:38873060
- 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
- 2025-08-09 07:06:00
@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}},
}