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Software to more rapidly and accurately predict protein-ligand binding affinities is of high interest for early-stage drug discovery, and physics-based methods are among the most widely used technologies for this purpose. The accuracy of these methods depends critically on the accuracy of the potential functions that they use. Potential functions are typically trained against a combination of quantum chemical and experimental data. However, although binding affinities are among the most important quantities to predict, experimental binding affinities have not to date been integrated into the experimental data set used to train potential functions. In recent years, the use of host-guest complexes as simple and tractable models of binding thermodynamics has gained popularity due to their small size and simplicity, relative to protein-ligand systems. Host-guest complexes can also avoid ambiguities that arise in protein-ligand systems such as uncertain protonation states. Thus, experimental host-guest binding data are an appealing additional data type to integrate into the experimental data set used to optimize potential functions. Here, we report the extension of the Open Force Field Evaluator framework to enable the systematic calculation of host-guest binding free energies and their gradients with respect to force field parameters, coupled with the curation of 126 host-guest complexes with available experimental binding free energies. As an initial application of this novel infrastructure, we optimized generalized Born (GB) cavity radii for the OBC2 GB implicit solvent model against experimental data for 36 host-guest systems. This refitting led to a dramatic improvement in accuracy for both the training set and a separate test set with 90 additional host-guest systems. The optimized radii also showed encouraging transferability from host-guest systems to 59 protein-ligand systems. However, the new radii are significantly smaller than the baseline radii and lead to excessively favorable hydration free energies (HFEs). Thus, users of the OBC2 GB model currently may choose between GB cavity radii that yield more accurate binding affinities and GB cavity radii that yield more accurate HFEs. We suspect that achieving good accuracy on both will require more far-reaching adjustments to the GB model. We note that binding free-energy calculations using the OBC2 model in OpenMM gain about a 10× speedup relative to corresponding explicit solvent calculations, suggesting a future role for implicit solvent absolute binding free-energy (ABFE) calculations in virtual compound screening. This study proves the principle of using host-guest systems to train potential functions that are transferrable to protein-ligand systems and provides an infrastructure that enables a range of applications.

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 work, 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 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 ΔHmix, ρ(x), ΔGsolv, and ΔGtrans. Additionally, we benchmarked against protein-ligand binding free energies (ΔGbind), 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.

A continuum of water populations can exist in nanoscale layered materials, which impacts transport phenomena relevant for separation, adsorption, and charge storage processes. Quantification and direct interrogation of water structure and organization are important in order to design materials with molecular-level control for emerging energy and water applications. Through combining molecular simulations with ambient-pressure X-ray photoelectron spectroscopy, X-ray diffraction, and diffuse reflectance infrared Fourier transform spectroscopy, we directly probe hydration mechanisms at confined and nonconfined regions in nanolayered transition-metal carbide materials. Hydrophobic (K+) cations decrease water mobility within the confined interlayer and accelerate water removal at nonconfined surfaces. Hydrophilic cations (Li+) increase water mobility within the confined interlayer and decrease water-removal rates at nonconfined surfaces. Solutes, rather than the surface terminating groups, are shown to be more impactful on the kinetics of water adsorption and desorption. Calculations from grand canonical molecular dynamics demonstrate that hydrophilic cations (Li+) actively aid in water adsorption at MXene interfaces. In contrast, hydrophobic cations (K+) weakly interact with water, leading to higher degrees of water ordering (orientation) and faster removal at elevated temperatures.

We explore the influence of the solvent dipole moment on cation-anion interactions and transport in 1-butyl-3-methyl-imidazolium bis(trifluoromethylsulfonyl), [BMIM + ][Tf2N - ]. Free energy profiles derived from atomistic molecular dynamics (MD) simulations show a correlation of the cation-anion separation and the equilibrium depth of the potential of mean force with the dipole moment of the solvent. Correlations of the ion diffusivity with the dipole moment and the concentration of the solvent were further demonstrated by classical MD simulations. Quasi-elastic neutron scattering experiments with deuterated solvents reveal a complex picture of nanophase separation into the ionic liquid-rich and solvent-rich phases. The experiment corroborates the trend of concentration- and dipole moment-dependent enhancement of ion mobility by the solvent, as suggested by the simulations. Despite the considerable structural complexity of ionic liquid-solvent mixtures, we can rationalize and generalize the trends governing ionic transport in these complex electrolytes.