Computational predictions of metal-macrocycle stability constants require accurate treatments of local solvent and pH effects

Rational design of molecular chelating agents requires a detailed understanding of physicochemical ligand-metal interactions in solvent phase. Computational quantum chemistry methods should be able to provide this, but computational reports have shown poor accuracy when determining absolute binding constants for many chelating molecules. To understand why, we compare and benchmark static- and dynamics-based computational procedures for a range of monovalent and divalent cations binding to a conventional cryptand molecule: 2.2.2-cryptand ([2.2.2]). The benchmarking comparison shows that dynamics simulations using standard OPLS-AA classical potentials can reasonably predict binding constants for monovalent cations, but these procedures fail for divalent cations. We also consider computationally efficient static procedure using Kohn-Sham density functional theory (DFT) and cluster-continuum modeling that accounts for local microsolvation and pH effects. This approach accurately predicts binding energies for monovalent and divalent cations with an average error of 3.2 kcal mol−1 compared to experiment. This static procedure thus should be useful for future molecular screening efforts, and high absolute errors in the literature may be due to inadequate modeling of local solvent and pH effects.

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Computational Predictions of Metal-Macrocycle Stability Constants Require Accurate Treatments of Local Solvent and pH Effects

10.26434/chemrxiv.13765819 ◽

2021 ◽

Author(s):

Brian Gentry ◽

Tae Hoon Choi ◽

William Belfield ◽

John Keith

Keyword(s):

Density Functional ◽

Rational Design ◽

Divalent Cations ◽

Binding Constants ◽

Binding Energies ◽

Average Error ◽

Ph Effects ◽

Computationally Efficient ◽

Metal Interactions ◽

Solvent Phase

Rational design of molecular chelating agents requires a detailed understanding of physicochemical ligand-metal interactions in solvent phase. Computational quantum chemistry methods should be able to provide this, but computational reports have shown poor accuracy when determining absolute binding constants for many chelating molecules. To understand why, we compare and benchmark static- and dynamics-based computational procedures for a range of monovalent and divalent cations binding to a conventional cryptand molecule: 2.2.2-cryptand ([2.2.2]). The benchmarking comparison shows that dynamics simulations using standard OPLS-AA classical potentials can reasonably predict binding constants for monovalent cations, but these procedures fail for divalent cations. We also consider computationally efficient static procedure using Kohn-Sham density functional theory (DFT) and cluster-continuum modeling that accounts for local microsolvation and pH effects. This approach accurately predicts binding energies for monovalent and divalent cations with an average error of 3.2 kcal mol−1 compared to experiment. This static procedure thus should be useful for future molecular screening efforts, and high absolute errors in the literature may be due to inadequate modeling of local solvent and pH effects.

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Computational Quantum Chemistry for Multiple-Site Heisenberg Spin Couplings Made Simple: Still Only One Spin–Flip Required

The Journal of Physical Chemistry Letters ◽

10.1021/acs.jpclett.5b00733 ◽

2015 ◽

Vol 6 (10) ◽

pp. 1982-1988 ◽

Cited By ~ 33

Author(s):

Nicholas J. Mayhall ◽

Martin Head-Gordon

Keyword(s):

Quantum Chemistry ◽

Multiple Site ◽

Heisenberg Spin ◽

Computational Quantum Chemistry ◽

Spin Flip ◽

Computational Quantum

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Rayleigh-Ritz Majorization Error Bounds for the Linear Response Eigenvalue Problem

Open Mathematics ◽

10.1515/math-2019-0052 ◽

2019 ◽

Vol 17 (1) ◽

pp. 653-667

Author(s):

Zhongming Teng ◽

Hong-Xiu Zhong

Keyword(s):

Eigenvalue Problem ◽

Quantum Chemistry ◽

Error Bounds ◽

Linear Response ◽

Computational Quantum Chemistry ◽

Matrix Anal ◽

Deflating Subspaces ◽

Important Notion ◽

Theoretical Foundations ◽

Computational Quantum

Abstract In the linear response eigenvalue problem arising from computational quantum chemistry and physics, one needs to compute a few of smallest positive eigenvalues together with the corresponding eigenvectors. For such a task, most of efficient algorithms are based on an important notion that is the so-called pair of deflating subspaces. If a pair of deflating subspaces is at hand, the computed approximated eigenvalues are partial eigenvalues of the linear response eigenvalue problem. In the case the pair of deflating subspaces is not available, only approximate one, in a recent paper [SIAM J. Matrix Anal. Appl., 35(2), pp.765-782, 2014], Zhang, Xue and Li obtained the relationships between the accuracy in eigenvalue approximations and the distances from the exact deflating subspaces to their approximate ones. In this paper, we establish majorization type results for these relationships. From our majorization results, various bounds are readily available to estimate how accurate the approximate eigenvalues based on information on the approximate accuracy of a pair of approximate deflating subspaces. These results will provide theoretical foundations for assessing the relative performance of certain iterative methods in the linear response eigenvalue problem.

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