Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field

Wang, Lingle; Wu, Yujie; Deng, Yu; Kim, Byungchan; Pierce, Levi C. T.; Krilov, Goran; Lupyan, Dmitry; Robinson, Shaughnessy; Dahlgren, Markus; Greenwood, Jeremy R.; Romero, Donna L.; Masse, C. E.; Knight, Jennifer K.; Steinbrecher, Thomas; Beuming, Thijs; Damm, Wolfgang; Harder, Ed; Sherman, Woody; Brewer, Mark; Wester, Ron; Murcko, Mark A.; Frye, Leah L.; Farid, Ramy; Lin, Teng-Yi; Mobley, David L.; Jorgensen, William L.; Berne, B. J.; Friesner, Richard A.; Abel, Robert

doi:10.1021/ja512751q

Cited by 1,049 publications

(2,022 citation statements)

References 49 publications

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“…Co. Ltd., ≥98%) and/or PES (Lianchuang Medical Chemistry Co., 98%), were added no more than 24 h before cell filling. The filled cells were sealed at −90 kPa using a compact vacuum sealer (MSK-115A, MTI Corp.) and immediately held at 1.5 V at room temperature (21)(22)(23)(24)(25) • C) to prevent Cu current collector corrosion during the ∼24 h wetting period that followed. Cells were then loaded into temperature-controlled boxes (40.0 ± 0.1…”

Section: Methodsmentioning

confidence: 99%

The Solid-Electrolyte Interphase Formation Reactions of Ethylene Sulfate and Its Synergistic Chemistry with Prop-1-ene-1,3-Sultone in Lithium-Ion Cells

Hall

Allen

Glazier

et al. 2017

J. Electrochem. Soc.

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Electrolyte additives are a promising route to stable solution chemistries needed for improved and next-generation lithium-ion cells. Yet the underlying chemistry remains unknown for most additives and additive blends in use. This work presents possible reaction pathways for solid-electrolyte interphase formation in lithium-ion cells from ethylene sulfate (DTD), prop-1-ene-1,3-sultone (PES) and the binary PES/DTD blend. Pathways are supported by theoretical calculations (density functional theory) and experimental results (electrochemistry, gas chromatography thermal conductivity detection, X-ray photoelectron spectroscopy, isothermal microcalorimetry). A hypothesis to understand the synergistic chemistry of the blend is proposed: Reduction of PES, the 'primary additive', at the negative electrode forms a nucleophile that reacts with electrophilic DTD, the 'secondary additive', to produce a passive solid-electrolyte interphase that inhibits direct reduction of DTD or the solvent. The results are further discussed in the contexts of future mechanistic studies, computational additive discovery, and the development of improved lithium-ion cell chemistries. Stable electrolyte solution chemistries are essential for the development of higher voltage, faster charging, and longer-living lithium-ion cells as well as next-generation cell technologies (e.g., sodium-ion and lithium-air).1-4 To avoid electrolyte decomposition, many batteries in use today operate at much lower cell voltages than the capability of the electrode materials, often leaving a significant fraction of the theoretical capacity unused. [5][6][7] In recent years, chemical additives have been used in research and commercially available cells to increase cell stability limits to higher voltages and temperatures, creating industrial and academic interest in further additive development. 5,6,[8][9][10][11][12][13][14][15][16][17][18] The potential to achieve cutting-edge performance with minimal changes to supply chains for electrolyte salts and solvents has tremendous practical appeal. However, the design of new additives for different applications still poses significant scientific challenges to which the emergence of additive blends in recent years adds further complexity. [19][20][21][22] Whereas the selection of new solution chemistries has traditionally followed a lengthy trial and error method, there is an increasing desire to streamline the process by applying modern calculations. High-throughput computational methods for the discovery of new compounds are by now well-developed, for example in the pharmaceutical field of drug discovery. [23][24][25] Quantum mechanical and molecular mechanics calculations have been suggested to offer similar potential for energy storage technology research, including the development of new electrode materials 26-28 and electrolyte solutions 29-32 for batteries. Yet before the computational discovery of new electrolyte additives may be realized, there is a need for a greater understanding of how additives function, at...

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Section: Methodsmentioning

confidence: 99%

The Solid-Electrolyte Interphase Formation Reactions of Ethylene Sulfate and Its Synergistic Chemistry with Prop-1-ene-1,3-Sultone in Lithium-Ion Cells

Hall

Allen

Glazier

et al. 2017

J. Electrochem. Soc.

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“…However, it does apparently require the estimate of an unknown translational, rotational, and conformational binding site volume V site in the complex 21 via an independent unrestrained simulation of the bound state or via an auxiliary FEP for turning on the restraint when the fully coupled ligand is in the binding site. 19,22,23 In a series of recent papers, Fujitani and co-workers, 24 The standard state correction issue can be bypassed altogether by computing relative binding free energies 3,28 due to the transmutation of an unrestrained ligand into another in the same binding site and in the solvent. Relative binding free energy calculations neglect altogether the possibility of a change of binding site volume due to the transmutation or even the possibility that the transmuting ligand may leave the binding site at some alchemical state.…”

Section: ■ Introductionmentioning

confidence: 99%

Statistical Mechanics of Ligand–Receptor Noncovalent Association, Revisited: Binding Site and Standard State Volumes in Modern Alchemical Theories

Procacci

Chelli

2017

J. Chem. Theory Comput.

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The present paper is intended to be a comprehensive assessment and rationalization, from a statistical mechanics perspective, of existing alchemical theories for binding free energy calculations of ligand−receptor systems. In detail, the statistical mechanics foundation of noncovalent interactions in ligand−receptor systems is revisited, providing a unifying treatment that encompasses the most important variants in the alchemical approaches from the seminal double annihilation method [Jorgensen et al. J. Chem. Phys. 1988; 89, 3742] ■ INTRODUCTIONThe determination of the binding free energy in ligand− receptor systems is the cornerstone of drug discovery. In the last few decades, traditional molecular docking techniques in computer-assisted drug design have been modified, integrated, or superseded using methodologies relying on a more realistic description of the drug−receptor system. It has become increasingly clear that a microscopic description of the solvent is a crucial ingredient to reliably rank the affinity of putative ligands for a given target. 1,2 In the framework of atomistic molecular dynamics (MD) simulations with explicit solvent, several methods for rigorously determining the absolute binding free energy in noncovalently bonded systems have been devised. Most of these methodologies are based on the socalled alchemical route 3−5 (see refs 6−9 for recent reviews). In this approach, proposed for the first time by Jorgensen et al. 10,11 and named double annihilation method (DAM), the absolute binding free energy of the ligand−receptor system was obtained by setting up a thermodynamic cycle as indicated in Figure 1 in which the basic quantities to be estimated are the annihilation or decoupling free energies of the ligand in the bound state and in bulk water, indicated hereafter as ΔG b and ΔG u , respectively. These decoupling free energies correspond to the two closing branches of the cycle and are obtained by discretizing the alchemical path connecting the fully interacting and fully decoupled ligand in a number of intermediate nonphysical states and then running MD simulations for each of these states.Alchemical states are defined by a λ coupling parameter entering the Hamiltonian, varying between 1 and 0, such that at λ = 1 and λ = 0 one has the fully interacting and gas-phase ligand, respectively. ΔG b and ΔG u are usually recovered as a sum of the contributions from each of the λ windows by applying the free energy perturbation method 12 (FEP). Alternatively, and equivalently, one can compute the canonical average of the derivative of the Hamiltonian at the discrete λ points, obtaining the decoupling free energy via numerical thermodynamic integration (TI). 13 Finally, the thermodynamic cycle is closed by computing the difference between the two decoupling free energies along the alchemical path, ΔG b and ΔG u , obtaining the dissociation free energy in solution.Gilson et al. 14 criticized Jorgensen's theory 10 by pointing out that the resulting dissociation free energy does not de...

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“…[9][10][11] The end goal of these studies is frequently to aid in the design of new drugs and therapeutic treatments. [12][13][14] Multiscale modeling approaches including long timescale molecular dynamics simulations to explore fluctuations and conformational changes of biomolecules, combined quantum mechanical/molecular mechanical (QM/MM) simulations to examine deeply embedded reactive chemical events, and implicit solvent calculations of small model reactions are used for this task.…”

Section: Introductionmentioning

confidence: 99%

VR-SCOSMO: A smooth conductor-like screening model with charge-dependent radii for modeling chemical reactions

Kuechler

Giese

York

2016

The Journal of Chemical Physics

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To better represent the solvation effects observed along reaction pathways, and of ionic species in general, a charge-dependent variable-radii smooth conductor-like screening model (VR-SCOSMO) is developed. This model is implemented and parameterized with a third order density-functional tight binding quantum model, DFTB3/3OB-OPhyd, a quantum method which was developed for organic and biological compounds, utilizing a specific parameterization for phosphate hydrolysis reactions. Unlike most other applications with the DFTB3/3OB model, an auxiliary set of atomic multipoles is constructed from the underlying DFTB3 density matrix which is used to interact the solute with the solvent response surface. The resulting method is variational, produces smooth energies, and has analytic gradients. As a baseline, a conventional SCOSMO model with fixed radii is also parameterized. The SCOSMO and VR-SCOSMO models shown have comparable accuracy in reproducing neutral-molecule absolute solvation free energies; however, the VR-SCOSMO model is shown to reduce the mean unsigned errors (MUEs) of ionic compounds by half (about 2-3 kcal/mol).

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Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field

Cited by 1,049 publications

References 49 publications

The Solid-Electrolyte Interphase Formation Reactions of Ethylene Sulfate and Its Synergistic Chemistry with Prop-1-ene-1,3-Sultone in Lithium-Ion Cells

The Solid-Electrolyte Interphase Formation Reactions of Ethylene Sulfate and Its Synergistic Chemistry with Prop-1-ene-1,3-Sultone in Lithium-Ion Cells

Statistical Mechanics of Ligand–Receptor Noncovalent Association, Revisited: Binding Site and Standard State Volumes in Modern Alchemical Theories

VR-SCOSMO: A smooth conductor-like screening model with charge-dependent radii for modeling chemical reactions

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