Semantic repurposing model for traditional Chinese ancient formulas based on a knowledge graph

Abstract

Drug repurposing can dramatically decrease cost and risk in drug discovery and it can be very helpful for recommending candidate drugs. However, as traditional Chinese medicine (TCM) formulas are multi-component, the repurposing methods for western medicine are usually not applicable for TCM formulas. In this study, we proposed a concept/strategy for multi-component formula/recipe discovery with network and semantics. With this concept, we establish a semantic formula-repurposing model for TCM based on a link-prediction algorithm and knowledge graph (KG). The proposed model integrating semantic embedding with KG networks facilitates the effective repurposing of traditional Chinese medicine formulas. First, we construct a KG that consists of more than 46 600 ancient formulas, including over 120 000 entities, 415 900 triples and 12 relations that are extracted from non-structural textual data by deep-learning techniques. Then, a link-prediction model is built on KG triplets for entity and edge semantic vectors. The formula-repurposing task is considered as computing the similarity of semantic vectors in KG between entities and query formulas. In the current version of the proposed model, two ways of repurposing are tested: one is searching for a similar formula to the query one, and the other is seeking a possible formula for rare, emerging diseases or epidemics. The former is based on the name of a formula; the latter is carried out through symptom entities. The experiments are exemplified with existing formulas, Fufang Danshen Tablets () and the symptoms of COVID-19. The results agree well with existing clinical practices. This suggests our model can be a comprehensive approach to constructing a knowledge graph of TCM formulas and a TCM formula-repurposing strategy, which is able to assist compound formula development and facilitate further research in multi-compound drug/prescription discovery.