Advancing physicochemical property predictions in computational drug discovery
Skip to main content
Open Access Publications from the University of California

UC Irvine

UC Irvine Electronic Theses and Dissertations bannerUC Irvine

Advancing physicochemical property predictions in computational drug discovery


Computer-aided drug design aims to guide the discovery of compounds with optimal pharmaceutical properties. Computational tools can evaluate large libraries of virtual molecules to help prioritize new compounds to synthesize and test. Properties such as protein-ligand binding affinity and physicochemical properties are of interest. To learn how reliable computational models are, it's necessary to evaluate the prediction accuracy of physicochemical property prediction. In Chapter 2, I describe my work in testing the accuracy of free energy calculations through partition coefficient predictions. In Chapter 3, I assess the accuracy of \pKa{} and partitioning predictions in a physical property prediction challenge. Additionally, I present work in which I developed and/or applied computational chemistry tools. In Chapter 4, I discuss my work on enhancing the sampling of water rearrangements through the extension of a hybrid simulation method. In Chapter 5, I describe work towards improving host-guest binding free energy calculations by refitting host force field parameters.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View