Exploration and Validation of Force Field Design Protocols through QM-to-MM Mapping

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 in the early stages can persist for many generations of the force field. We hypothesise that careful use of quantum mechanics to inform molecular mechanics parameter derivation (QM-to-MM mapping) should be used to significantly reduce the number of parameters that require fitting to experiment and increase the pace of force field development. Here, we design a collection of 15 new protocols for small, organic molecule force field design, and test their accuracy against experimental liquid properties. Our best performing model has only seven fitting parameters, yet achieves mean unsigned errors of just 0.031 g/cm3 and 0.69 kcal/mol in liquid densities and heats of vaporisation, compared to experiment. The software required to derive the designed force fields is freely available at https://github.com/qubekit/QUBEKit.

Improving Small Molecule Force Fields by Identifying and Characterizing Small Molecules with Inconsistent Parameters

Many molecular simulation methods use force fields to help model and simulate molecules and their behavior in various environments. Force fields are sets of functions and parameters used to calculate the potential energy of a chemical system as a function of the atomic coordinates. Despite the widespread use of force fields, their inadequacies are often thought to contribute to systematic errors in molecular simulations. Furthermore, different force fields tend to give varying results on the same systems with the same simulation settings. Here, we present a pipeline for comparing the geometries of small molecule conformers. We aimed to identify molecules or chemistries that are particularly informative for future force field development because they display inconsistencies between force fields. We applied our pipeline to a subset of the eMolecules database, and highlighted molecules that appear to be parameterized inconsistently across different force fields. We then identified over-represented functional groups in these molecule sets. The molecules and moieties identified by this pipeline may be particularly helpful for future force field parameterization.

Improving Small Molecule Force Fields by Identifying and Characterizing Small Molecules with Inconsistent Parameters

Many molecular simulation methods use force fields to help model and simulate molecules and their behavior in various environments. Force fields are sets of functions and parameters used to calculate the potential energy of a chemical system as a function of the atomic coordinates. Despite the widespread use of force fields, their inadequacies are often thought to contribute to systematic errors in molecular simulations. Furthermore, different force fields tend to give varying results on the same systems with the same simulation settings. Here, we present a pipeline for comparing the geometries of small molecule conformers. We aimed to identify molecules or chemistries that are particularly informative for future force field development because they display inconsistencies between force fields. We applied our pipeline to a subset of the eMolecules database, and highlighted molecules that appear to be parameterized inconsistently across different force fields. We then identified over-represented functional groups in these molecule sets. The molecules and moieties identified by this pipeline may be particularly helpful for future force field parameterization.

Data-driven analysis of the number of Lennard-Jones types needed in a force field

<p>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.</p><p>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>

Data-driven analysis of the number of Lennard-Jones types needed in a force field

<p>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.</p><p>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>