openforcefield/openff-forcefields: Version 2.0.0 "Sage"

Wagner, Jeff; Thompson, Matt; Dotson, David; hyejang,; SimonBoothroyd,; Rodríguez-Guerra, Jaime

doi:10.5281/zenodo.5214478

Cited by 11 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using regularized least squares optimization with the L-BFGS-B algorithm [32], we minimized an objective function that captures the ability of a parameter set to reproduce physical property observables. Using this framework, we studied the benefits of including physical property data of mixtures in training LJ parameters [26], then applied that training method to a production force field, OpenFF 2.0.0 "Sage" [33].…”

Section: Non-bonded Training Is Expensive and Difficultmentioning

confidence: 99%

Using physical property surrogate models to perform multi-fidelity global optimization of force field parameters

Madin

Shirts

2022

Preprint

View full text Add to dashboard Cite

Dispersion-repulsion interactions, commonly represented in atomistic force fields by the Lennard-Jones (LJ) potential, play an important role in the accuracy of molecular simulations. Training the force field parameters used in the LJ potential is challenging, generally requiring adjustment based on simulations of macroscopic physical properties. The computational expense of these simulations limits the size of training data set and number of optimization steps that can be taken, often requiring modelers to perform optimizations within a local parameter region. To allow for global LJ parameter optimization against large training sets, we introduce a multi-fidelity optimization technique which uses Gaussian process surrogate modeling to build inexpensive models of physical properties as a function of LJ parameters. This allows for fast evaluation of objective functions, greatly accelerating searches over parameter space. We use an iterative framework which performs global optimization at the surrogate level, followed by validation at the simulation level and surrogate refinement. Using this technique on two previously studied training sets, containing up to 195 physical property targets, we refit a subset of the LJ parameters for the OpenFF 1.0.0 ``Parsley'' force field. We demonstrate that this multi-fidelity technique can find improved parameter sets compared to a purely simulation-based optimization by searching more broadly and escaping local minima. In most cases, these parameter sets are transferable to other similar molecules in a test set. This multi-fidelity technique provides a platform for fast optimization against physical properties that can be refined and applied in multiple ways to the development of molecular models.

show abstract

Section: Non-bonded Training Is Expensive and Difficultmentioning

confidence: 99%

Using physical property surrogate models to perform multi-fidelity global optimization of force field parameters

Madin

Shirts

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In an attempt to counter this trend, in a new line of general FFs, the Open Force Field (OpenFF) Initiative has replaced atom-typed parameter encodings with a technique termed direct chemical perception. , The chemical perception framework assigns parameters via standard chemical substructure queries implemented in the SMARTS language. This removes many redundancies, for example, in equivalent parameters that would otherwise be applied to different combinations of atom types, and allows the OpenFF line of FFs (Parsley, Sage, etc.) to be very compact without sacrificing accuracy (Table ).…”

Section: Introductionmentioning

confidence: 99%

“… 10 , 13 The chemical perception framework assigns parameters via standard chemical substructure queries implemented in the SMARTS language. This removes many redundancies, for example, in equivalent parameters that would otherwise be applied to different combinations of atom types, and allows the OpenFF line of FFs (Parsley, 10 Sage, 6 etc.) to be very compact without sacrificing accuracy ( Table 1 ).…”

Section: Introductionmentioning

confidence: 99%

Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale

Horton

Boothroyd²,

Wagner

et al. 2022

J. Chem. Inf. Model.

View full text Add to dashboard Cite

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).

show abstract

“…Parameter type MMFF [5] OPLS3 [5] OPLS3e [4] Sage (OpenFF 2.0.0) [6] Bond Such an approach to transferable FF design is often successful as evidenced by numerous retrospective [7][8][9][10] and prospective [11,12] studies, which show good agreement between experiment and simulation. Critical applications of FFs include alchemical free energy calculations, which have become a widespread, relatively low-cost computational tool to aid the identification and development of high binding affinity small molecules in the early stages of drug discovery campaigns [11].…”

Section: Introductionmentioning

confidence: 99%

“…The chemical perception framework assigns parameters via standard chemical substructure queries implemented in the SMARTS language. This removes many redundancies, for example in equivalent parameters that would otherwise be applied to different combinations of atom types, and allows the OpenFF line of FFs (Parsley [10], Sage [6], ...) to be very compact without sacrificing accuracy (Table 1). Given the hierarchical nature of these FFs, their extension becomes trivial.…”

Section: Introductionmentioning

confidence: 99%

Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization At Scale

Horton

Boothroyd²,

Wagner³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The development of accurate transferable force fields is key to realizing the full potential of atomistic modelling 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 progressively yield accuracy improvements. However, force field torsion parameters, which must effectively account for many stereoelectronic and steric effects, are generally 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 drug-like fragments. We use BespokeFit to derive individual torsion parameters for each of these molecules, thereby reducing the root mean squared 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 0.56$^{0.77}_{0.39}$ to 0.42$^{0.59}_{0.28}$ kcal/mol and R$^2$ correlation improved from 0.72$^{0.87}_{0.35}$ to 0.93$^{0.97}_{0.84}$).

show abstract

openforcefield/openff-forcefields: Version 2.0.0 "Sage"

Cited by 11 publications

References 0 publications

Using physical property surrogate models to perform multi-fidelity global optimization of force field parameters

Using physical property surrogate models to perform multi-fidelity global optimization of force field parameters

Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale

Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization At Scale

Contact Info

Product

Resources

About