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Updated: Feb 9, 2026

Heterogeneous Removal of Water-Soluble Ruthenium Olefin Metathesis Catalyst from Aqueous Media Via Host-Guest Interaction
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A Linear Interaction Energy Model for Cavitand Host-Guest Binding Affinities.

Joel José Montalvo-Acosta1, Paulina Pacak1, Diego Enry Barreto Gomes1,2,3

  • 1Laboratoire d'Ingénierie des Fonctions Moléculaires, UMR 7177 CNRS , Université de Strasbourg , F-67083 Strasbourg Cedex, France.

The Journal of Physical Chemistry. B
|June 5, 2018
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Summary

We developed a linear interaction energy (LIE) model for predicting host-guest binding affinities. This model achieves high accuracy across diverse host-guest systems using molecular dynamics simulations.

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Area of Science:

  • Supramolecular Chemistry
  • Computational Chemistry
  • Molecular Modeling

Background:

  • Host-guest systems are crucial for understanding molecular recognition in solution.
  • These systems have applications in drug delivery and chemical sensing.
  • Predicting binding affinity is essential for designing new host-guest systems.

Purpose of the Study:

  • To develop and validate a Linear Interaction Energy (LIE) model for predicting host-guest binding affinities.
  • To assess the efficiency and predictive power of the LIE model across various host families and guests.
  • To identify key factors influencing prediction accuracy, such as host strain energy.

Main Methods:

  • Utilized molecular dynamics (MD) simulations for host-guest systems.
  • Applied a Linear Interaction Energy (LIE) model to calculate binding affinities.
  • Tested the model on four host families (cucurbituril, octa acid, β-cyclodextrin) and 49 diverse guests.
  • Investigated the transferability of LIE model parameters and force-field independence.

Main Results:

  • Achieved root mean square errors <1.5 kcal/mol for binding affinity predictions compared to experimental data.
  • Demonstrated high predictive power with only a few nanoseconds of MD simulations.
  • Showed that LIE model parameters are transferable across different host-guest families.
  • Found that including host strain energy significantly improves prediction accuracy, especially for similarly sized hosts and guests.

Conclusions:

  • The developed LIE model offers an efficient and accurate method for predicting host-guest binding affinities.
  • The model's performance is robust across various host families and largely independent of the chosen force field.
  • Host strain energy is a critical factor for accurate predictions in host-guest systems.
  • Further improvements are needed for highly charged and bulky guests, indicating future research directions.