Biomedical subjects
Publications and source records attributed to Natalie Fey.
A molecular mechanics force field has been developed which accurately reproduces experimental solid state structures and conformer interconversion barriers for a series of sterically congested diaryl and triaryl phosphines and some of their chalcogenide and Cr(CO)5 derivatives.
Square-planar Pt(II) complexes of the bis(mesitylimino)acenaphthene (mesBIAN) ligand are emissive from a MMLL'CT excited state in a dichloromethane solution at room temperature. Investigation of the nature of the frontier orbitals in these near-IR emitters by a combination of emission spectroscopy, electron paramagnetic resonance spectroscopy, and density functional theory calculations suggests that emission is enabled by the presence of low-lying ligand pi* orbitals on the mesBIAN.
A prototype collection of knowledge on ligands in metal complexes, termed a ligand knowledge base (LKB), has been developed. This contribution describes the design of DFT-calculated descriptors for monodentate phosphorus(III) donor ligands in a range of representative complexes. Using the resulting data, a ligand space is mapped and predictive models are derived for metal complexes. Important characteristics, including chemical, computational and statistical robustness for the generation and exploitation of such an LKB are described. Chemical robustness ensures transferability of the descriptors, as well as comprehensive sampling of ligand space. To make the calculations amenable to automation in an e-science setting, a reliable, well-defined computational approach has been sought from which the descriptors can be readily extracted. The LKB has been explored with multivariate statistical methods. Principal component analysis (PCA) is used for the mapping of chemical space, projecting multiple descriptors into scatter plots which illustrate the clustering of chemically similar ligands. Interpretation of the resulting principal components in terms of established steric and electronic properties and the importance of its statistical robustness to variations in the ligand set are discussed. Multiple linear regression (MLR) models have been derived, demonstrating the versatility of the descriptors for modeling varied experimentally determined parameters (bond lengths, reaction enthalpies and bond-stretching frequencies). The importance of re-sampling methods for testing the robustness of predictions is highlighted. A strategy for the construction of a robust LKB suitable for the modeling of ligand and complex behavior is outlined based on these observations.
The ligand field molecular mechanics (LFMM) model, which incorporates the ligand field stabilization energy (LFSE) directly into the potential energy expression of molecular mechanics (MM), has been implemented in the "chemically aware" molecular operating environment (MOE) software package. The new program, christened DommiMOE, is derived from our original in-house code that has been linked to MOE via its applications programming interface and a number of other routines written in MOE's native scientific vector language (SVL). DommiMOE automates the assignment of atom types and their associated parameters and popular force fields available in MOE such as MMFF94, AMBER, and CHARMM can be easily extended to provide a transition metal simulation capability. Some of the unique features of the LFMM are illustrated using MMFF94 and some simple [MCl)]2- and [Ni(NH3)n]2+ species. These studies also demonstrate how density functional theory calculations, especially on experimentally inaccessible systems, provide important data for designing improved LFMM parameters. DommiMOE treats Jahn-Teller distortions automatically, and can compute the relative energies of different spin states for Ni(II) complexes using a single set of LFMM parameters.
The local density approximation and a range of nonhybrid gradient corrected density functionals (PW91, BLYP, PBE, revPBE, RPBE) have been assessed with respect to the prediction of geometries and spin-state energy preferences for a range of homoleptic Fe(II)L6 and Fe(III)L6 complexes, where L = Cl-, CN-, NH3, pyridine, imidazole, H2O, O=CH2 and tetrahydrofuran. While the qualitative spin-state energies from in vacuo structure optimizations are reasonable the geometries are relatively poorly treated, especially for [FeCl6]3-/4-. Structural results for all the complexes are significantly improved by including environmental effects. The best compromise between structural and spin-state predictive accuracy was obtained for the RPBE functional in combination with the COSMO solvation approach. This approach systematically overestimates the energetic preference for a low spin state, which is partly due to the well-known effect of the lack of exact exchange in nonhybrid functionals and partly due to the larger solvation stabilization of low-spin complexes that have shorter bond lengths and thus smaller molecular volumes than their high-spin partners. Calculations on low spin [Fe(bipy)3]2+ and [Fe(phen)3]2+ and their ortho methyl substituted analogs, which are high spin at room temperature but cross over to low spin at low temperature, suggest the RPBE/COSMO combination generates low spin states which are too stable by approximately 13 kcal mol(-1).
To account for the distortion of the coordination sphere that takes place in complexes containing open-shell metal cations such as Cu(II), we implemented, in sum of interactions between fragments ab initio computed (SIBFA) molecular mechanics, an additional contribution to take into account the ligand field splitting of the metal d orbitals. This term, based on the angular overlap model, has been parameterized for Cu(II) coordinated to oxygen and nitrogen ligands. The comparison of the results obtained from density functional theory computations on the one hand and SIBFA or SIBFA-LF on the other shows that SIBFA-LF gives geometric arrangements similar to those obtained from quantum mechanical computations. Moreover, the geometric improvement takes place without downgrading the energetic agreement obtained from SIBFA. The systems considered are Cu(II) interacting with six water molecules, four ammonia or four imidazoles, and four water plus two formate anions.
A protocol for the partially automated computational investigation of crystal structure geometries of transition-metal complexes with unusual/outlier structural features has been developed for application in an e-science context. This protocol not only is envisaged as a part of knowledge base software packages such as Mogul but can also be used to further analyze the results of database searches. The issues arising from automating the initial input generation and DFT optimization of complexes have been examined and a procedure for extracting additional knowledge "value" from the computational results is described. Potential problems/weaknesses arising from the choice of computational approach and from errors in the crystal structure refinement are discussed. A range of likely outcomes of applying this protocol to database mining results is illustrated, with representative examples identified for tetracoordinate transition-metal complexes and ligand fragments (terminal chloride, monodentate phosphorus(III), and primary amine ligands) with unusual metal-ligand bond lengths.
A range of different statistical models has been fitted to experimental data for the Tolman electronic parameter (TEP) based on a large set of calculated descriptors in a prototype ligand knowledge base (LKB) of phosphorus(III) donor ligands. The models have been fitted by ordinary least squares using subsets of descriptors, principal component regression, and partial least squares which use variables derived from the complete set of descriptors, least angle regression, and the least absolute shrinkage and selection operator. None of these methods is robust against outliers, so we also applied a robust estimation procedure to the linear regression model. Criteria for model evaluation and comparison have been discussed, highlighting the importance of resampling methods for assessing the robustness of models and the scope for making predictions in chemically intuitive models. For the ligands covered by this LKB, ordinary least squares models of descriptor subsets provide a good representation of the data, while partial least squares, principal component regression, and least angle regression models are less suitable for our dual aims of prediction and interpretation. A linear regression model with robustly fitted parameters achieves the best model performance over all classes of models fitted to TEP data, and the weightings assigned to ligands during the robust estimation procedure are chemically intuitive. The increased model complexity when compared to the ordinary least squares linear model is justified by the reduced influence of individual ligands on the model parameters and predictions of new ligands. Robust linear regression models therefore represent the best compromise for achieving statistical robustness in simple, chemically meaningful models.