>Magister Scientiae - MSc / The growing resistance of Methicillin-Resistant Staphylococcus aureus (MRSA) to currently prescribed drugs has resulted in the failure of prevention and treatment of different infections caused by the superbug. Therefore, to keep pace with the resistance, there is a pressing need for novel antimicrobial agents, especially from non-conventional sources. Several natural products (NPs) have displayed varying in vitro activities against the pathogen but few of these natural compounds have been studied for their prospects to be potential antimicrobial drug candidates. This may be due to the high cost, tedious, and time-consuming process of conducting the important preclinical tests on these compounds. Hence, there is a need for cost-effective strategies for mining the available data on these natural compounds. This would help to get the knowledge that may guide rational prioritization of “likely to succeed” natural compounds to be developed into potential antimicrobial drug candidates. Cheminformatic approaches in drug discovery enable chemical data mining, in conjunction with unsupervised and supervised learning from available bioactivity data that may unlock the full potential of NPs in antimicrobial drug discovery. Therefore, taking advantage of the available NPs with their known in vitro activity against MRSA, this study conducted cheminformatic and data mining analysis towards hit profiling, hit-prioritization, hit-optimization, and target prediction of anti-MRSA NPs. Cheminformatic profiling was conducted on the 111 anti-MRSA NPs (AMNPs) retrieved from literature. About 20 current drugs for MRSA (CDs) were used as a reference to identify AMNPs with promising prospects to become drug candidates.

The budding yeast Saccharomyces cerevisiae has been an invaluable model organism in contributing to the current understanding of cellular biology, owing mainly to its highly tractable genetic system and the completion of its genome sequencing in 1996. Indeed, these bolstered the development of novel methods that have provided great insights into genetic and protein networks in human cells. With the large collection of datasets, S. cerevisiae also became an ideal platform for investigating the mechanism of action of novel compounds. The first part of my thesis uses a validated chemogenomic assay to investigate the mechanism of action of structurally related novel DNA-damaging agents, delineating valuable structure-activity relationship in the process. The second part describes the development of a method that uses drug-induced wild-type growth dynamic to characterize novel compounds, which, in combination with the chemogenomic assay, may complement existing high throughput screening experiments to improve the current drug development process.

The budding yeast Saccharomyces cerevisiae has been an invaluable model organism in contributing to the current understanding of cellular biology, owing mainly to its highly tractable genetic system and the completion of its genome sequencing in 1996. Indeed, these bolstered the development of novel methods that have provided great insights into genetic and protein networks in human cells. With the large collection of datasets, S. cerevisiae also became an ideal platform for investigating the mechanism of action of novel compounds. The first part of my thesis uses a validated chemogenomic assay to investigate the mechanism of action of structurally related novel DNA-damaging agents, delineating valuable structure-activity relationship in the process. The second part describes the development of a method that uses drug-induced wild-type growth dynamic to characterize novel compounds, which, in combination with the chemogenomic assay, may complement existing high throughput screening experiments to improve the current drug development process.