Density functional calculations on "catch and release" complexes of C60 with corannulene derived molecular bowls show that computationally obtained (1) H nuclear magnetic resonance (NMR) chemical shifts can be used as a reliable predictor of binding constants. A wide range of functionals was benchmarked against accurate ab initio calculations to ensure a credible representation of the weak forces that dominate the interactions in these systems. The most reliable density functional theory (DFT) results were then calibrated using experimentally observed NMR data. Careful analysis and comparison of a wide range of commonly used density functionals shows that the explicit inclusion of dispersion corrections is currently the only reliable way to accurately describe the systems investigated in our study. Moreover, we are able to show that the B97-D and ωB97X-D functionals are not only able to reproduce ab initio benchmark calculations, but they do so accurately with a moderately sized basis sets and without the problems of numerical integration we encountered with other functionals in this study.

Bond orders and formal charges are fundamental chemical descriptors. In cheminformatic applications it is necessary to be able to assign these properties to a given molecular structure automatically, given minimal input information. Here we describe a method for determining the bond order and formal charge assignments from only the atom types and connectivity. Our method utilises a graph theoretical description of electron positions. Each electron position assignment is scored according to lookup tables of atomic and bond dissociation energies derived from quantum chemical calculations. We tested three different optimisation methods—local optimisation, an A* pathfinding method, and an FPT optimisation method utilising tree decompositions—for finding the best electron position assignment, from which the bond orders and formal charges are extracted. We show that our method can assign bond orders and formal charges at a high degree of accuracy across a wide range of molecules from two different databases, and that the FPT algorithm provides the best combination of speed and accuracy.Electronic supplementary materialThe online version of this article (10.1186/s13321-019-0340-0) contains supplementary material, which is available to authorized users.

Amphiphilic nanostructures of ionic liquids are retained to high solute concentrations and the partitioning of solutes within these nanostructures can be rationally influenced by ion selection.

Molecular simulations allow investigation of the structure, dynamics and thermodynamics of molecules at an atomic level of detail, and as such, are becoming increasingly important across many areas of science. As the range of applications increases, so does the variety of molecules. Simulation of a new type of molecule requires generation of parameters that result in accurate representation of the behavior of that molecule, and, in most cases, are compatible with existing parameter sets. While many automated parametrization methods exist, they are in general not well suited to large and conformationally dynamic molecules. We present here a method for automated assignment of parameters for large, novel biomolecules, and demonstrate its usage for peptides of varying degrees of complexity. Our method uses a graph theoretic representation to facilitate matching of the target molecule to molecular fragments for which reliable parameters are available. It requires minimal user input and creates parameter files compatible with the widely-used GROMACS simulation software.