In silico approaches in the study of traditional Chinese herbal medicine

Abstract

Recent development of Systems Biology in this b omicsb era reinforced the therapeutic strategy of considering human systems as a whole. Multi-herb prescriptions have been routinely used in TCM formulated by using TCM-defined herbal properties (TCM-HPs) where the scientific basis is unclear. Machine learning methods (MLMs) are used to explore the scientific basis of TCM prescription formulation. The studies reveal that MLMs are capable of classifying TCM prescriptions and herb pairs from those of random herb combinations showing that there is hidden scientific rule in the formulation of TCM prescriptions. Besides, a structural approach using inverse docking method (INVDOCK) is used to identify putative metastatic-related targets of Rhubarb anthraquinones from a protein structure database. The results implicate additive or synergistic effects of Rhubarb anthraquinones in anti-metastasis when used in combinations. In addition, current study of herbal synergism using literature-based approach reveals multiple mechanisms that involve either similar or distinct molecular targets as well as signaling pathways. In general, current in silico approaches used in this study covered both traditional and molecular aspects of TCM from top-down and bottom-up directions.