Doctor of Philosophy

dissertationSelectivity in chemical reactions is a matter of distinguishing between pathways of little energetic difference. From reactions affording no selectivity in product formation to those achieving selectivity levels of >99:1, the energy differences responsible for these disparate isomer ratios range from 0 to ~3 kcal mol-1, respectively. It is astounding that such a seemingly trivial amount of energy, on the order of the energetic barrier to carbon-carbon bond rotation in ethane (~2.9 kcal mol-1), can precipitate products in exquisitely high isomeric purity. Identifying the origin of the small energy differences that afford selectivity has, historically, been a daunting endeavor and predominantly characterized by empiricism. In recent years, the Sigman group has been developing a more efficient alternative to the typical guess-and-check approach to optimizing catalyst-substrate interactions for high site- and enantioselective outcomes. This methodology relies on the quantification and systematic modulation of various reaction features that putatively induce selectivity, ultimately enabling the identification of mathematical equations to describe these effects. Detailed herein is the process for developing reliably predictive mathematical constructs of reaction selectivity. In the context of three distinct reactions-iridium-catalyzed asymmetric hydrogenation (Chapter 2), rhodium-catalyzed site-selective C-H amination (Chapter 3), and rhodium-catalyzed asymmetric transfer hydrogenation (Chapter 4)-means for effective model development are put forth. Namely, this work describes the examination of the unconventional application of design of experiments principles, the identification of parameters capable of describing selectivity, and the process by which linear regression models are developed and validated. Through this approach, mathematical equations are developed that relate the differential free energy of selectivity to numerical depictions of steric, electronic, and hydrophobic effects. By identifying underlying predictive trends, developed models serve as a unique avenue by which mechanistic insight may be gained about selectivity engendering interactions. Consequently, these models enable the energetic optimization of substrate-catalyst interactions and the quantitative prediction of how such changes will influence reaction selectivity. Through the work of myself and my colleagues in the Sigman group, we are learning how reactions may be investigated and understood so as to make the ~3 kcal mol"1 energy range that is responsible for selectivity a vast window of opportunity for shaping reaction partners to achieve desired reaction outcomes

Doctor of Philosophy

dissertationAsymmetric catalysis is a powerful method for synthesizing enantiomerically enriched chiral building blocks. Detailed understanding of how catalysts impart facial bias on prochiral substrates has the potential to enable improved catalyst design and increase catalyst applicability. To this end, linear free energy relationships have been used to relate catalyst properties to enantioselectivity, enabling greater understanding of key catalyst-substrate interactions. Linear free energy relationships also can allow prediction of catalyst performance prior to their preparation. In this dissertation, several linear free energy relationships are described with a focus on developing predictive power and understanding the mechanism of asymmetric induction. In asymmetric catalysis, steric effects are often implicated as key components in imparting enantioselectivity; however, they are typically treated empirically. In Chapter 2, steric parameters, particularly Charton parameters, are used to quantify ligand steric effects in the Nozaki-Hiyama-Kishi allylation of aryl aldehydes and ketones. Multidimensional linear free energy relationships, which simultaneously quantified the steric effects at both positions, are determined and used to predict ligand performance. The multivariate linear free energy relationships have guided the design of a new ligand scaffold capable of enantioselective propargylation of ketones, which is discussed in Chapter 3. The multivariate relationships were expanded to include nonsteric terms, which enabled the development of an electronically and sterically optimized catalytic system for the enantioselective propargylation of ketones, yielding enantioenriched homopropargyl alcohols. The multivariate approach to describing substituent effects in asymmetric catalysis led to the evaluation of Sterimol parameters. Chapter 4 gives five examples of data sets where Sterimol values led to better correlation and predictive power than the previously used Charton parameters. The computational basis of the Sterimol parameters allows for greater interpretation of the models in which they are utilized. Quantifying the factors that lead to enantioselective outcomes is a key challenge in asymmetric catalysis. Combining steric parameters, multidimensional analysis, and the principles of experimental design can lead to increased predictive power in asymmetric catalysis

Molecular surface area measures of polarity and hydrogen bonding for QSAR

Modifications were made to the traditional PSA descriptor by decoupling it into its H-bond acidic and basic components. The PSA based descriptors were also scaled according to the known hydrogen bonding characteristics of common functional groups to make them more realistic measures of a molecules hydrogen bonding capacity. Three other surface area descriptors total surface area, total halogen atom surface area and total aromatic carbon surface area were also defined. Various routes to the calculation of these descriptors were explored and it was concluded the best descriptors were those obtained from a single structure generated using the semi empirical-method AMI. It was also shown that descriptors obtained from a vdw surface were more suitable than those obtained from solvent accessible surface area. The scaled PSA descriptors were initially tested against octanol-water, chloroform-water, and cyclohexane-water partition coefficients of 110 organic and drug-like molecules. All of the models produced were seen to be statistically accurate and followed known characteristics of the partition coefficients considered. The scaled PSA descriptors were then applied successfully to a number of important biological processes such as cellular uptake and intestinal absorption models were also produced for important industrial processes such as Fluorophilicity and CMC. The surface area descriptors were also seen to be equally capable of modelling inorganic molecules and excellent models were produced for octanol-water and chloroform-water partitions for a number of platinum containing drugs.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Molecular surface area measures of polarity and hydrogen bonding for QSAR

Modifications were made to the traditional PSA descriptor by decoupling it into its H-bond acidic and basic components. The PSA based descriptors were also scaled according to the known hydrogen bonding characteristics of common functional groups to make them more realistic measures of a molecules hydrogen bonding capacity. Three other surface area descriptors total surface area, total halogen atom surface area and total aromatic carbon surface area were also defined. Various routes to the calculation of these descriptors were explored and it was concluded the best descriptors were those obtained from a single structure generated using the semi empirical-method AMI. It was also shown that descriptors obtained from a vdw surface were more suitable than those obtained from solvent accessible surface area. The scaled PSA descriptors were initially tested against octanol-water, chloroform-water, and cyclohexane-water partition coefficients of 110 organic and drug-like molecules. All of the models produced were seen to be statistically accurate and followed known characteristics of the partition coefficients considered. The scaled PSA descriptors were then applied successfully to a number of important biological processes such as cellular uptake and intestinal absorption models were also produced for important industrial processes such as Fluorophilicity and CMC. The surface area descriptors were also seen to be equally capable of modelling inorganic molecules and excellent models were produced for octanol-water and chloroform-water partitions for a number of platinum containing drugs

SMARTS Approach to Chemical Data Mining and Physicochemical Property Prediction.

The calculation of physicochemical and biological properties is essential in order to facilitate modern drug discovery. Chemical spaces dimensionalized by these descriptors have been used to scaffold-hop in order to discover new lead and drug-like molecules. Broadening the boundaries of structure based drug design, these molecules are expected to share the same physiological target and have similar efficacy, as do known drug molecules sharing the same region in chemical property space. In the past few decades physicochemical and ADMET (absorption, distribution, metabolism, elimination, and toxicity) property predictors have been the subject of increased focus in academia and the pharmaceutical industry. Due to the ever increasing attention given to data mining and property predictions, we first discuss the sources of experimental pKa values and current methodologies used for pKa prediction in proteins and small molecules. Of particular concern is an analysis of the scope, statistical validity, overall accuracy, and predictive power of these methods. The expressed concerns are not limited to predicting pKa, but apply to all empirical predictive methodologies. In a bottom-up approach, we explored the influence of freely generated SMARTS string representations of molecular fragments on chelation and cytotoxicity. Later investigations, involving the derivation of predictive models, use stepwise regression to determine the optimal pool of SMARTS strings having the greatest influence over the property of interest. By applying a unique scoring system to sets of highly generalized SMARTS strings, we have constructed well balanced regression trees with predictive accuracy exceeding that of many published and commercially available models for cytotoxicity, pKa, and aqueous solubility. The methodology is robust, extremely adaptable, and can handle any molecular dataset with experimental data. This story details our struggles of data gathering, curation, and the development of a machine learning methodology able to derive and validate highly accurate regression trees capable of extremely fast property predictions. Regression trees created by our method are well suited to calculate descriptors for large in silico molecular libraries, facilitating data mining of chemical spaces in search of new lead molecules in drug discovery.Ph.D.Medicinal ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64627/1/adamclee_1.pd