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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.

Deciphering the activity of chemical molecules against a pathogenic organism is an essential task in drug discovery process. Virtual screening, in which few plausible molecules are selected from a large set for further processing using computational methods, has become an integral part and complements the expensive and time-consuming in vivo and in vitro experiments. To this end, it is essential to extract certain features from molecules which in the one hand are relevant to the biological activity under consideration, and on the other are suitable for designing fast and robust algorithms. The features/representations are derived either from physicochemical properties or their structures in numerical form and are known as descriptors. In this work we develop two new molecular-fragment descriptors based on the critical analysis of existing descriptors. This development is primarily guided by the notion of coding degeneracy, and the ordering induced by the descriptor on the fragments. One of these descriptors is derived based on the simple graph representation of the molecule, and attempts to encode topological feature or the connectivity pattern in a hierarchical way without discriminating atom or bond types. Second descriptor extends the first one by weighing the atoms (vertices) in consideration with the bonding pattern, valence state and type of the atom. Further, the usefulness of these indices is tested by ranking and classifying molecules in two previously studied large heterogeneous data sets with regard to their anti-tubercular and other bacterial activity. This is achieved by developing a scoring function based on clustering using these new descriptors. Clusters are obtained by ordering the descriptors of training set molecules, and identifying the regions which are (almost) exclusively coming from active/inactive molecules. To test the activity of a new molecule, overlap of its descriptors in those cluster (interpolation) is weighted. Our results are found to be superior compared to previous studies: we obtained better classification performance by using only structural information while previous studies used both structural features and some physicochemical parameters. This makes our model simple, more interpretable and less vulnerable to statistical problems like chance correlation and over fitting. With focus on predictive modeling, we have carried out rigorous statistical validation. New descriptors utilize primarily the topological information in a hierarchical way. This can have significant implications in the design of new bioactive molecules (inverse QSAR, combinatorial library design) which is plagued by combinatorial explosion due to use of large number of descriptors. While the combinatorial generation of molecules with desirable properties is still a problem to be satisfactorily solved, our model has potential to reduce the number of degrees of freedom, thereby reducing the complexity.

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.

<strong>Chapter 1</strong> Provides an overview of drug discovery with particular emphasis on library selection and hit identification methods using virtual based approaches. <strong>Chapter 2</strong> Gives an outline of the bone morphogenetic protein (BMP) signalling pathway and literature BMP pathway modulators. The association between the regulation of BMP pathway and cardiomyogenesis is also described. <strong>Chapter 3</strong> Describes the use of ligand based virtual screening to discover small molecule activators of the BMP signalling pathway. A robust cell based BMP responsive gene activity reporter assay was developed to test the libraries of small molecules selected. Hit molecules from the screen were synthesised to validate activity. It was found that a group of known histone deacetylase (HDAC) inhibitors displayed most promising activity. These were evaluated in a secondary assay measuring the expression of two BMP pathway regulated genes, hepcidin and Id1, using reverse transcription polymerase chain reaction (RT-PCR). 188 was discovered to increase expression of both BMP-responsive genes. <strong>Chapter 4</strong> Provides an overview of existing cannabinoid receptor (CBR) modulating molecules and their connection to progression of atherosclerosis. <strong>Chapter 5</strong> Outlines the identification and optimisation of selective small molecule agonists acting at the cannabinoid 2 receptor (CB₂R). Ligand based virtual screen was undertaken and promising hits were synthesised to allow structure activity relationship (SAR) to be developed around the hit molecule providing further information of the functional groups tolerated at the active site. Subsequent studies led to the investigation and optimisation of physicochemical properties around 236 leading to the development of a suitable compound for in vivo testing. Finally, a CB₂R selective compound with favourable physicochemical properties was evaluated in vivo in a murine inflammation model and displayed reduced recruitment of monocytes to the site of inflammation.