Exploration and validation of force field design protocols through QM-to-MM mapping

Ringrose, Chris; Horton, Joshua; Wang, Lee‐Ping; Cole, D. J. A.

doi:10.1039/d2cp02864f

Cited by 10 publications

(17 citation statements)

References 83 publications

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“…As for the predicted densities, while the result for methanol is not far off the experimental value, the water model significantly underestimates the liquid experimental density. Such discrepancies are somewhat to be expected since the repulsion/dispersion parameters for QUBE were trained , over a wide range of compounds using the under-polarized liquid reference state (i.e., from a dielectric continuum calculation). Fully incorporating the more accurate reference state for the liquid-phase dipole moment that we propose here into the QUBE workflow would require an SCEE calculation to be carried out on each molecule of the QUBE training set (25 molecules in total), followed by a full refitting of the force field parameters .…”

Section: Resultsmentioning

confidence: 99%

“…Such discrepancies are somewhat to be expected since the repulsion/dispersion parameters for QUBE were trained , over a wide range of compounds using the under-polarized liquid reference state (i.e., from a dielectric continuum calculation). Fully incorporating the more accurate reference state for the liquid-phase dipole moment that we propose here into the QUBE workflow would require an SCEE calculation to be carried out on each molecule of the QUBE training set (25 molecules in total), followed by a full refitting of the force field parameters . Although SCEE provides a good balance between accuracy and computational speed for the calculation of realistic liquid-phase dipole moments, it is still too time-consuming to apply to such a large number of molecules within a reasonable time frame.…”

Section: Resultsmentioning

confidence: 99%

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What is the Optimal Dipole Moment for Nonpolarizable Models of Liquids?

Jorge

Barrera

Milne

et al. 2023

J. Chem. Theory Comput.

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In classical nonpolarizable models, electrostatic interactions are usually described by assigning fixed partial charges to interaction sites. Despite the multitude of methods and theories proposed over the years for partial charge assignment, a fundamental question remainswhat is the correct degree of polarization that a fixed-charge model should possess to provide the best balance of interactions (including induction effects) and yield the best description of the potential energy surface of a liquid phase? We address this question by approaching it from two separate and independent viewpoints: the QUantum mechanical BEspoke (QUBE) approach, which assigns bespoke force field parameters for individual molecules from ab initio calculations with minimal empirical fitting, and the Polarization-Consistent Approach (PolCA) force field, based on empirical fitting of force field parameters with an emphasis on transferability by rigorously accounting for polarization effects in the parameterization process. We show that the two approaches yield consistent answers to the above question, namely, that the dipole moment of the model should be approximately halfway between those of the gas and the liquid phase. Crucially, however, the reference liquid-phase dipole needs to be estimated using methods that explicitly consider both mean-field and local contributions to polarization. In particular, continuum dielectric models are inadequate for this purpose because they cannot account for local effects and therefore significantly underestimate the degree of polarization of the molecule. These observations have profound consequences for the development, validation, and testing of nonpolarizable models.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

What is the Optimal Dipole Moment for Nonpolarizable Models of Liquids?

Jorge

Barrera

Milne

et al. 2023

J. Chem. Theory Comput.

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“…We hope that this work and the open procedures described in this paper facilitate further force field development cycles both within OpenFF and in the broader community. We anticipate considerable room for future fixed-charge force fields of the form employed here, but many other avenues for further work are open as well; for example, off-site charges are likely important in certain key chemical environments and should be explored 112,114,115 and polarizable force fields [116][117][118] are also of considerable interest. We hope OpenFF infrastructure and datasets can provide a foundation for further exploration in these areas as well.…”

Section: Discussionmentioning

confidence: 99%

Development and Benchmarking of Open Force Field 2.0.0 — the Sage Small Molecule Force Field

Boothroyd¹,

Behara

Madin

et al. 2022

Preprint

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We introduce the Open Force Field (OpenFF)~2.0.0 small molecule force field for drug-like molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF force fields are based on direct chemical perception, which generalizes easily to highly diverse sets of chemistries based on substructure queries. Like the previous OpenFF iterations, the Sage generation of OpenFF force fields was validated in protein-ligand simulations to be compatible with AMBER biopolymer force fields. In this paper we detail the methodology used to develop this force field, as well as the innovations and improvements introduced since the release of Parsley 1.0.0. One particularly significant feature of Sage is a set of improved Lennard-Jones (LJ) parameters retrained against condensed phase mixture data, the first refit of LJ parameters in the OpenFF small molecule force field line. Sage also includes valence parameters refit to a larger database of quantum chemical calculations than previous versions, as well as improvements in how this fitting is performed. Force field benchmarks show improvements in general metrics of performance against quantum chemistry reference data such as root mean square deviations (RMSD) of optimized conformer geometries, torsion fingerprint deviations (TFD), and improved relative conformer energetics (ΔΔ𝐸). We present a variety of benchmarks for these metrics against our previous force fields as well as in some cases other small molecule biomolecular force fields. Sage also demonstrates improved performance in estimating physical properties, including comparison against experimental data from various thermodynamic databases for small molecule properties such as Δ𝐻_𝑚𝑖𝑥, ρ(𝑥), Δ𝐺_𝑠𝑜𝑙𝑣 and Δ𝐺_𝑡𝑟𝑎𝑛𝑠. Additionally, we benchmarked against protein-ligand binding free energies (Δ𝐺_𝑏𝑖𝑛𝑑), where Sage yields results statistically similar to previous force fields. All the data is made publicly available along with complete details on how to reproduce the training results at https://github.com/openforcefield/openff-sage.

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“… 4 This allows users to fit new torsion parameters for novel chemistry that is poorly represented by the general FF using a consistent parametrization method. Several other tools also aid the fitting of bespoke torsion parameters to QM potential energy surfaces; these include QUBEKit, 17 , 18 paramol, 19 parmfit, 20 qforce, 21 JOYCE, 22 DFFR, 23 Rotational Profiler, 24 or the algebraic method of Kania, 25 to name a few. Although bespoke torsion parameters have the potential to improve the accuracy of molecular simulations, fitting these parameters to multiple QM torsion scans can significantly slow down the parameter assignment stage for users.…”

Section: Introductionmentioning

confidence: 99%

Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale

Horton

Boothroyd²,

Wagner

et al. 2022

J. Chem. Inf. Model.

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The development of accurate transferable force fields is key to realizing the full potential of atomistic modeling in the study of biological processes such as protein–ligand binding for drug discovery. State-of-the-art transferable force fields, such as those produced by the Open Force Field Initiative, use modern software engineering and automation techniques to yield accuracy improvements. However, force field torsion parameters, which must account for many stereoelectronic and steric effects, are considered to be less transferable than other force field parameters and are therefore often targets for bespoke parametrization. Here, we present the Open Force Field QCSubmit and BespokeFit software packages that, when combined, facilitate the fitting of torsion parameters to quantum mechanical reference data at scale. We demonstrate the use of QCSubmit for simplifying the process of creating and archiving large numbers of quantum chemical calculations, by generating a dataset of 671 torsion scans for druglike fragments. We use BespokeFit to derive individual torsion parameters for each of these molecules, thereby reducing the root-mean-square error in the potential energy surface from 1.1 kcal/mol, using the original transferable force field, to 0.4 kcal/mol using the bespoke version. Furthermore, we employ the bespoke force fields to compute the relative binding free energies of a congeneric series of inhibitors of the TYK2 protein, and demonstrate further improvements in accuracy, compared to the base force field (MUE reduced from 0.560.39 0.77 to 0.420.28 0.59 kcal/mol and R 2 correlation improved from 0.720.35 0.87 to 0.930.84 0.97).

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Exploration and validation of force field design protocols through QM-to-MM mapping

Abstract: The scale of the parameter optimisation problem in traditional molecular mechanics force field construction means that design of a new force field is a long process, and sub-optimal choices made...

Cited by 10 publications

References 83 publications

What is the Optimal Dipole Moment for Nonpolarizable Models of Liquids?

What is the Optimal Dipole Moment for Nonpolarizable Models of Liquids?

Development and Benchmarking of Open Force Field 2.0.0 — the Sage Small Molecule Force Field

Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale

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