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      Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale

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          Abstract

          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 0.56 0.39 0.77 to 0.42 0.28 0.59 kcal/mol and R 2 correlation improved from 0.72 0.35 0.87 to 0.93 0.84 0.97).

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          A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu.

          The method of dispersion correction as an add-on to standard Kohn-Sham density functional theory (DFT-D) has been refined regarding higher accuracy, broader range of applicability, and less empiricism. The main new ingredients are atom-pairwise specific dispersion coefficients and cutoff radii that are both computed from first principles. The coefficients for new eighth-order dispersion terms are computed using established recursion relations. System (geometry) dependent information is used for the first time in a DFT-D type approach by employing the new concept of fractional coordination numbers (CN). They are used to interpolate between dispersion coefficients of atoms in different chemical environments. The method only requires adjustment of two global parameters for each density functional, is asymptotically exact for a gas of weakly interacting neutral atoms, and easily allows the computation of atomic forces. Three-body nonadditivity terms are considered. The method has been assessed on standard benchmark sets for inter- and intramolecular noncovalent interactions with a particular emphasis on a consistent description of light and heavy element systems. The mean absolute deviations for the S22 benchmark set of noncovalent interactions for 11 standard density functionals decrease by 15%-40% compared to the previous (already accurate) DFT-D version. Spectacular improvements are found for a tripeptide-folding model and all tested metallic systems. The rectification of the long-range behavior and the use of more accurate C(6) coefficients also lead to a much better description of large (infinite) systems as shown for graphene sheets and the adsorption of benzene on an Ag(111) surface. For graphene it is found that the inclusion of three-body terms substantially (by about 10%) weakens the interlayer binding. We propose the revised DFT-D method as a general tool for the computation of the dispersion energy in molecules and solids of any kind with DFT and related (low-cost) electronic structure methods for large systems.
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            Density-functional thermochemistry. III. The role of exact exchange

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              Effect of the damping function in dispersion corrected density functional theory.

              It is shown by an extensive benchmark on molecular energy data that the mathematical form of the damping function in DFT-D methods has only a minor impact on the quality of the results. For 12 different functionals, a standard "zero-damping" formula and rational damping to finite values for small interatomic distances according to Becke and Johnson (BJ-damping) has been tested. The same (DFT-D3) scheme for the computation of the dispersion coefficients is used. The BJ-damping requires one fit parameter more for each functional (three instead of two) but has the advantage of avoiding repulsive interatomic forces at shorter distances. With BJ-damping better results for nonbonded distances and more clear effects of intramolecular dispersion in four representative molecular structures are found. For the noncovalently-bonded structures in the S22 set, both schemes lead to very similar intermolecular distances. For noncovalent interaction energies BJ-damping performs slightly better but both variants can be recommended in general. The exception to this is Hartree-Fock that can be recommended only in the BJ-variant and which is then close to the accuracy of corrected GGAs for non-covalent interactions. According to the thermodynamic benchmarks BJ-damping is more accurate especially for medium-range electron correlation problems and only small and practically insignificant double-counting effects are observed. It seems to provide a physically correct short-range behavior of correlation/dispersion even with unmodified standard functionals. In any case, the differences between the two methods are much smaller than the overall dispersion effect and often also smaller than the influence of the underlying density functional. Copyright © 2011 Wiley Periodicals, Inc.
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                Author and article information

                Journal
                J Chem Inf Model
                J Chem Inf Model
                ci
                jcisd8
                Journal of Chemical Information and Modeling
                American Chemical Society
                1549-9596
                1549-960X
                09 November 2022
                28 November 2022
                : 62
                : 22
                : 5622-5633
                Affiliations
                []School of Natural and Environmental Sciences, Newcastle University , Newcastle upon TyneNE1 7RU, United Kingdom
                []Boothroyd Scientific Consulting Ltd. , 71-75 Shelton Street, LondonWC2H 9JQ, Greater London, United Kingdom
                [§ ]The Open Force Field Initiative, Open Molecular Software Foundation , Davis, California95616, United States
                []Department of Chemistry, University of California , Irvine, California92697, United States
                []Department of Pharmaceutical Sciences, University of California , Irvine, California92697, United States
                [# ]Cresset , New Cambridge House, Bassingbourn Road, LitlingtonSG8 0SS, Cambridgeshire, United Kingdom
                []Computational & Systems Biology Program, Sloan Kettering Institute , Memorial Sloan Kettering Cancer Center, New York, New York10065, United States
                [° ]Department of Chemistry, Lancaster University , LancasterLA1 4YW, United Kingdom
                Author notes
                Author information
                https://orcid.org/0000-0001-8694-7200
                https://orcid.org/0000-0001-5131-7583
                https://orcid.org/0000-0003-0542-119X
                https://orcid.org/0000-0003-1721-0330
                https://orcid.org/0000-0003-2933-0719
                Article
                10.1021/acs.jcim.2c01153
                9709916
                36351167
                8a805bba-54a0-457d-a59c-0c210d964491
                © 2022 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 14 September 2022
                Funding
                Funded by: National Institutes of Health, doi 10.13039/100000002;
                Award ID: R01GM132386
                Funded by: Engineering and Physical Sciences Research Council, doi 10.13039/501100000266;
                Award ID: EP/T022167/1
                Funded by: UK Research and Innovation, doi 10.13039/100014013;
                Award ID: MR/T019654/1
                Categories
                Article
                Custom metadata
                ci2c01153
                ci2c01153

                Computational chemistry & Modeling
                Computational chemistry & Modeling

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