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When applied to drug discovery, modern computational systems can provide insight into the highly complex systems underlying drug activity and predict compounds or targets of interest. Many tools have been developed for computer aided drug discovery (CADD), focusing on small molecule ligands, protein targets, or both. The aim of this thesis is the improvement of CADD tools for describing small molecule properties and application of CADD to several stages of drug discovery regarding two targets for the treatment of obesity and related diseases: the neuropeptide Y4 receptor (Y4R) and the melanocortin-4 receptor (MC4R). In the first chapter, the major categories of CADD are outlined, including descriptions for many of the popular tools and examples where these tools have directly contributed to the discovery of new drugs. Following the introduction, several improvements for encoding stereochemistry and signed property distribution are introduced and tested in scenarios meant to simulate applications in virtual high-throughput screening. Y4R and MC4R are both class A G-protein coupled receptors (GPCRs) with endogenous peptide ligands that play critical roles in the signaling of satiety and energy metabolism. So far, no structures from either receptor family have been experimentally elucidated. CADD was combined with high-throughput screening (HTS) to discover the first small molecule positive allosteric modulators (PAMs) of Y4R. Secondly, CADD techniques were used to model the interaction of Y4R and pancreatic polypeptide based on experimental results that elucidate specific binding contacts. Similar SB-CADD approaches were used to model the interaction of MC4R with its high affinity peptide agonist α-MSH. Due to its role in monogenic forms of obesity, these models were used to predict which residues directly participate in binding and correlate mutated residues with their potential role in the binding site. |
