Automated Transition State Search and Its Application to Diverse Types of Organic Reactions.

Transition state search is at the center of multiple types of computational chemical predictions related to mechanistic investigations, reactivity and regioselectivity predictions, and catalyst design. The process of finding transition states in practice is, however, a laborious multistep operation that requires significant user involvement. Here, we report a highly automated workflow designed to locate transition states for a given elementary reaction with minimal setup overhead. The only essential inputs required from the user are the structures of the separated reactants and products. The seamless workflow combining computational technologies from the fields of cheminformatics, molecular mechanics, and quantum chemistry automatically finds the most probable correspondence between the atoms in the reactants and the products, generates a transition state guess, launches a transition state search through a combined approach involving the relaxing string method and the quadratic synchronous transit, and finally validates the transition state via the analysis of the reactive chemical bonds and imaginary vibrational frequencies as well as by the intrinsic reaction coordinate method. Our approach does not target any specific reaction type, nor does it depend on training data; instead, it is meant to be of general applicability for a wide variety of reaction types. The workflow is highly flexible, permitting modifications such as a choice of accuracy, level of theory, basis set, or solvation treatment. Successfully located transition states can be used for setting up transition state guesses in related reactions, saving computational time and increasing the probability of success. The utility and performance of the method are demonstrated in applications to transition state searches in reactions typical for organic chemistry, medicinal chemistry, and homogeneous catalysis research. In particular, applications of our code to Michael additions, hydrogen abstractions, Diels-Alder cycloadditions, carbene insertions, and an enzyme reaction model involving a molybdenum complex are shown and discussed.

Multiconformation, Density Functional Theory-Based pKa Prediction in Application to Large, Flexible Organic Molecules with Diverse Functional Groups.

We consider the conformational flexibility of molecules and its implications for micro- and macro-pKa. The corresponding formulas are derived and discussed against the background of a comprehensive scientific and algorithmic description of the latest version of our computer program Jaguar pKa, a density functional theory-based pKa predictor, which is now capable of acting on multiple conformations explicitly. Jaguar pKa is essentially a complex computational workflow incorporating research and technologies from the fields of cheminformatics, molecular mechanics, quantum mechanics, and implicit solvation models. The workflow also makes use of automatically applied empirical corrections which account for the systematic errors resulting from the neglect of explicit solvent interactions in the algorithm's implicit solvent model. Applications of our program to large, flexible organic molecules representing several classes of functional groups are shown, with a particular emphasis in illustrations laid on drug-like molecules. It is demonstrated that a combination of aggressive conformational search and an explicit consideration of multiple conformations nearly eliminates the dependence of results on the initially chosen conformation. In certain cases this leads to unprecedented accuracy, which is sufficient for distinguishing stereoisomers that have slightly different pKa values. An application of Jaguar pKa to proton sponges, the pKa of which are strongly influenced by steric effects, showcases the advantages that pKa predictors based on quantum mechanical calculations have over similar empirical programs.

Intermediates in dioxygen activation by methane monooxygenase: a QM/MM study.

Protein effects in the activation of dioxygen by methane monooxygenase (MMO) were investigated by using combined QM/MM and broken-symmetry Density Functional Theory (DFT) methods. The effects of a novel empirical scheme recently developed by our group on the relative DFT energies of the various intermediates in the catalytic cycle are investigated. Inclusion of the protein leads to much better agreement between the experimental and computed geometric structures for the reduced form (MMOH(red)). Analysis of the electronic structure of MMOH(red) reveals that the two iron atoms have distinct environments. Different coordination geometries tested for the MMOH(peroxo) intermediate reveal that, in the protein environment, the mu-eta2,eta2 structure is more stable than the others. Our analysis also shows that the protein helps to drive reactants toward products along the reaction path. Furthermore, these results demonstrate the importance of including the protein environment in our models and the usefulness of the QM/MM approach for accurate modeling of enzymatic reactions. A discrepancy remains in our calculation of the Fe-Fe distance in our model of HQ as compared to EXAFS data obtained several years ago, for which we currently do not have an explanation.

Integrated Modeling Program, Applied Chemical Theory (IMPACT).

We provide an overview of the IMPACT molecular mechanics program with an emphasis on recent developments and a description of its current functionality. With respect to core molecular mechanics technologies we include a status report for the fixed charge and polarizable force fields that can be used with the program and illustrate how the force fields, when used together with new atom typing and parameter assignment modules, have greatly expanded the coverage of organic compounds and medicinally relevant ligands. As we discuss in this review, explicit solvent simulations have been used to guide our design of implicit solvent models based on the generalized Born framework and a novel nonpolar estimator that have recently been incorporated into the program. With IMPACT it is possible to use several different advanced conformational sampling algorithms based on combining features of molecular dynamics and Monte Carlo simulations. The program includes two specialized molecular mechanics modules: Glide, a high-throughput docking program, and QSite, a mixed quantum mechanics/molecular mechanics module. These modules employ the IMPACT infrastructure as a starting point for the construction of the protein model and assignment of molecular mechanics parameters, but have then been developed to meet specialized objectives with respect to sampling and the energy function.

Computational insights on the challenges for polymerizing polar monomers.

Taking Pd di-imine catalysts as an example, we use first principles density functional theory (B3LYP/6-31G) to investigate the chain propagation steps for polymerization of polar monomers. We start with the complex formed from insertion of ethylene into the polymer chain and consider insertion into the Pd-C bond for each of four polar monomers: methyl acrylate, vinyl acetate, vinyl chloride, and acrylonitrile. We find 2,1-insertion is favored in each case (by 3 to 5 kcal/mol), resulting in a product with a strong interaction of the polar group for the growing polymer chain with the metal. Next, we insert another unit of the same polar monomer or an ethylene unit (except for acrylonitrile). We optimize the structures for all important intermediates and transition states using a continuum dielectric to account for solvation effects. These studies pinpoint the critical difficulties in designing catalysts to polymerize polar monomers.