7 research outputs found
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Towards Improved Force-Field Accuracy for Calculation of Binding Thermodynamics
Molecular dynamics simulations have revolutionized chemistry by allowing cheap and fast in silico analyses of numerous systems of interest. Despite various advancements, many applications still require costly experimental input in order to guide direction, as the results from simulations are not yet accurate enough to rely on alone. This is particularly problematic in drug design, where accurate binding affinity measurements could greatly improve the ability to discover drugs. In this thesis, I discuss a philosophy behind improving force-fields, the functions which provide the configurational energies in simulations, for binding calculations. I show a new approach to non-bonded force-field parameterization, which reduces the number of parameters used and also preserves the chemical uniqueness of each atom in a molecule. Secondly, I discuss a synthesis pathway towards generating novel host molecules for parameterization of force-fields built for binding calculations. Lastly, I present a novel and systematic analysis of experimental uncertainties in isothermal titration calorimetry data, to establish a clearer foundation for their use in force field parameterization. Taken together, these efforts contribute to an overall goal of developing force-fields that yield more accurate binding calculations
Data-driven analysis of the number of Lennard-Jones types needed in a force field
We optimized force fields with smaller and
larger sets of chemically motivated Lennard-Jones types against the
experimental properties of organic liquids. Surprisingly, we obtained results as good as or
better than those from much more complex typing schemes from exceedingly
simple sets of LJ types; e.g. a model with only two types of hydrogen and
only one type apiece for carbon, nitrogen and oxygen.The results justify sharply limiting the number of
parameters to be optimized in future force field development work, thus
reducing the risks of overfitting and the difficulties of reaching a global
optimum in the multidimensional parameter space. They thus increase our chances of arriving at well-optimized
force fields that will improve predictive accuracy, with applications in
biomolecular modeling and computer-aided drug design. The results also prove the feasibility and value of a
rigorous, data-driven approach to advancing the science of force field
development.</p
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Data-driven analysis of the number of Lennard–Jones types needed in a force field
Force fields used in molecular simulations contain numerical parameters, such as Lennard-Jones (LJ) parameters, which are assigned to the atoms in a molecule based on a classification of their chemical environments. The number of classes, or types, should be no more than needed to maximize agreement with experiment, as parsimony avoids overfitting and simplifies parameter optimization. However, types have historically been crafted based largely on chemical intuition, so current force fields may contain more types than needed. In this study, we seek the minimum number of LJ parameter types needed to represent key properties of organic liquids. We find that highly competitive force field accuracy is obtained with minimalist sets of LJ types; e.g. two H types and one type apiece for C, O, and N atoms. We also find that the fitness surface has multiple minima, which can lead to local trapping of the optimizer
Data-driven analysis of the number of Lennard–Jones types needed in a force field
Force fields used in molecular simulations contain numerical parameters, such as Lennard-Jones (LJ) parameters, which are assigned to the atoms in a molecule based on a classification of their chemical environments. The number of classes, or types, should be no more than needed to maximize agreement with experiment, as parsimony avoids overfitting and simplifies parameter optimization. However, types have historically been crafted based largely on chemical intuition, so current force fields may contain more types than needed. In this study, we seek the minimum number of LJ parameter types needed to represent key properties of organic liquids. We find that highly competitive force field accuracy is obtained with minimalist sets of LJ types; e.g. two H types and one type apiece for C, O, and N atoms. We also find that the fitness surface has multiple minima, which can lead to local trapping of the optimizer
Optimized Mapping of Gas-Phase Quantum Calculations to General Force Field Lennard-Jones Parameters Based on Liquid-State Data
We utilize a previously
described Minimal Basis Iterative Stockholder (MBIS) method to carry out an
atoms-in-molecules partitioning of electron densities. Information from these atomic densities is
then mapped to Lennard-Jones parameters using a set of mapping parameters much
smaller than the typical number of atom types in a force field. This approach
is advantageous in two ways: it eliminates atom types by allowing each atom to
have unique Lennard-Jones parameters, and it greatly reduces the number of parameters
to be optimized. We show that this approach yields results comparable to those obtained
with the typed GAFF force field, even when trained on a relatively small amount
of experimental data
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Data-Driven Mapping of Gas-Phase Quantum Calculations to General Force Field Lennard-Jones Parameters.
Molecular dynamics simulations are helpful tools for a range of applications, ranging from drug discovery to protein structure determination. The successful use of this technology largely depends on the potential function, or force field, used to determine the potential energy at each configuration of the system. Most force fields encode all of the relevant parameters to be used in distinct atom types, each associated with parameters for all parts of the force field, typically bond stretches, angle bends, torsions, and nonbonded terms accounting for van der Waals and electrostatic interactions. Much attention has been paid to the nonbonded parameters and their derivation, which are important in particular due to their governance of noncovalent interactions, such as protein-ligand binding. Parametrization involves adjusting the nonbonded parameters to minimize the error between simulation results and experimental properties, such as heats of vaporization and densities of neat liquids. In this setting, determining the best set of atom types is far from trivial, and the large number of parameters to be fit for the atom types in a typical force field can make it difficult to approach a true optimum. Here, we utilize a previously described Minimal Basis Iterative Stockholder (MBIS) method to carry out an atoms-in-molecules partitioning of electron densities. Information from these atomic densities is then mapped to Lennard-Jones parameters using a set of mapping parameters much smaller than the typical number of atom types in a force field. This approach is advantageous in two ways: it eliminates atom types by allowing each atom to have unique Lennard-Jones parameters, and it greatly reduces the number of parameters to be optimized. We show that this approach yields results comparable to those obtained with the typed GAFF 1.7 force field, even when trained on a relatively small amount of experimental data