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Computational Chemistry, Drug Design, and Drug Re-Discovery Aimed at Theory-Directed Synthesis of Cannabinoids and Computational Methods to Predict Bioactivity from Ligand Structure


The aim of this thesis is the discovery or re-discovery of drug-like compounds having therapeutic value; although two distinct strategies are reported, both share a strong dependence on computational chemistry.

The first project to be reported describes synthetic strategies applied to the synthesis of cannabinoids; successful strategies evolved from computational analysis of cannabinoid isomerization via pericyclic reactions. This project exemplifies a strategy designed to predict the best ligand for a specific receptor protein.

The second project reports our strategy to classify drugs according to their predicted indication. Here, structure of a drug-like compound is the only required input. The output will then report the indication class that is predicted to best describe the compound's drug-activity.

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