Novel cheminformatics tools to drive experimental discovery

The field of ``cheminformatics'' assists and guides research in drug discovery,materials science, and biology by providing tools for understanding andnavigating chemical data. Applications of cheminformatic tools often requiredata-specific solutions, which are developed and applied in this thesis. First,in response to drug discovery-oriented chemical data trending towards morecomplexity (in terms of magnitude and dimensionality), methods for validationin hit discovery and application of similarity and statistical models onmassive chemical datasets are developed here. Second, novel inhibitors ofSARS-CoV-2 infection are developed with a focused cheminformatic pipeline,providing compounds with promise for treating COVID-19. Third, natural productchemical space is cataloged and structurally parsed to guide syntheticdevelopment for macrocyclic peptides. A publicly available implementation of apeptide parsing algorithm is reported.