Traditional medicines (TM) have been used for centuries to treat illnesses, but in many cases
their modes-of-action (MOAs) remain unclear. Given the increasing data of chemical
ingredients of traditional medicines and the availability of large-scale bioactivity data linking
chemical structures to activities against protein targets, we are now in a position to propose
computational hypotheses for the MOAs using in silico target prediction. The MOAs were
established from supporting literature. The in silico target prediction, which is based on the
“Molecular Similarity Principle”, was modelled via two models: a Naïve Bayes Classifier and
a Random Forest Classifier. Chapter 2 discovered the relationship of 46 traditional Chinese
medicine (TCM) therapeutic action subclasses by mapping them into a dendrogram using the
predicted targets. Overall, the most frequent top three enriched targets/pathways were
immune-related targets such as tyrosine-protein phosphatase non-receptor type 2 (PTPN2)
and digestive system such as mineral absorption. Two major protein families, G-protein
coupled receptor (GPCR), and protein kinase family contributed to the diversity of the
bioactivity space, while digestive system was consistently annotated pathway motif. Chapter 3
compared the chemical and bioactivity space of 97 anti-cancer plants’ compounds of TCM,
Ayurveda and Malay traditional medicine. The comparison of the chemical space revealed
that benzene, anthraquinone, flavone, sterol, pentacyclic triterpene and cyclohexene were the
most frequent scaffolds in those TM. The annotation of the bioactivity space with target
classes showed that kinase class was the most significant target class for all groups. From a
phylogenetic tree of the anti-cancer plants, only eight pairs of plants were phylogenetically
related at either genus, family or order level. Chapter 4 evaluated synergy score of pairwise
compound combination of Shexiang Baoxin Pill (SBP), a TCM formulation for myocardial
infarction. The score was measured from the topological properties, pathway dissimilarity and
mean distance of all the predicted targets of a combination on a representative network of the
disease. The method found four synergistic combinations, ginsenoside Rb3 and cholic acid,
ginsenoside Rb2 and ginsenoside Rb3, ginsenoside Rb3 and 11-hydroxyprogesterone and
ginsenoside Rb2 and ginsenoside Rd agreed with the experimental results. The modulation of
androgen receptor, epidermal growth factor and caspases were proposed for the synergistic
actions. Altogether, in silico target prediction was able to discover the bioactivity space of
different TMs and elucidate the MOA of multiple formulations and two major health
concerns: cancer and myocardial infarction. Hence, understanding the MOA of the traditional
medicine could be beneficial in providing testable hypotheses to guide towards finding new
molecular entities
