Quantitative Models for Accurate Reactivity Predictions and Mechanistic Elucidation




Lu, Jingru

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Accurate prediction of reaction outcomes is among the most important goals in chemical and pharmaceutical synthesis. In recent years, the ultrafast growth in computing power and the advancement of high-throughput experimental (HTE) technology have paved new ways to apply data-rich approaches in chemistry research. In organic synthesis, data-driven methods have found many successful applications in accelerating reaction condition optimization and developing machine learning models for reaction prediction. Despite all the impressive progress made in this area, accurate prediction of chemical reactivity remains challenging. This thesis describes development of quantitative reactivity models for accurate reaction prediction and mechanistic elucidation in organic synthesis. Starting with a minireview/perspective in Chapter 1, recent progress is discussed in data-rich approaches to reaction development and quantitative predictions for palladium-catalyzed reaction systems. In Chapter 2 and Chapter 3, quantitative predictive models are developed for two pharmaceutically important reaction systems: nucleophilic aromatic substitution (SNAr) and oxidative addition to palladium(0), a fundamental and usually the rate/selectivity determining step in palladium-catalyzed cross-coupling reactions. Both models focus on structure-reactivity relationships of the electrophiles. Diverse and reliable reaction rate data for training set was collected using high-throughput competition experimentation. These were used to construct multivariate linear regression models by quantitatively mapping a group of ground state molecular descriptors to the experimental reaction rates. Predictive accuracy is validated via a series of random train-test splits, as well as predicting outcomes for a wide variety of external reaction data. Following the procedures described above, generally applicable models for quantitative predictions on both the reaction rates and site-selectivity for both reaction systems have been realized. In addition to making quantitative reaction predictions, a structure-reactivity model constructed using high-quality data and mechanistically meaningful descriptors is also very useful in gaining mechanistic insights. This is demonstrated by the solvent effect study in Chapter 4 and the reaction mechanistic study in Chapter 5. From the quantitative reactivity scales constructed for oxidative addition to palladium(0) in different solvents, specific electrophiles were identified that exhibit significant solvent effects; the role of solvent was investigated case by case. These include the importance of solvent hydrogen-bond basicity as well as solvent polarity. Finally, the underlying mechanistic causes behind a series of systematic prediction outliers from our oxidative addition model were investigated. These reveal that the frontier orbital symmetry also plays an important role in determining reaction outcomes. Insights into these mechanistic aspects, which have a significant impact on both the reaction rate and site-selectivity in oxidative addition to palladium(0), enabled a refined quantitative model that incorporates frontier orbital descriptors.



palladium oxidative addition, quantitative reaction prediction, nucleophilic aromatic substitution, reaction mechanistic elucidation, DFT transition state calculation