The present application relates to the technical field of
drug screening, and in particular to a method and
system for enhancing the ability of a large
language model to answer questions of a specific field. The method comprises the following steps: using a large
language model to extract a relation triple from a
data set, wherein the
data set comprises structured data and
unstructured data; on the basis of the relation triple, constructing a
knowledge graph of
G protein coupled receptors (GPCRs), and storing the
knowledge graph in a form of a
graph database, wherein the
knowledge graph comprises a GPCR entity and a relation thereof; using a pre-trained large
language model to search the
graph database for related information of a specific field to obtain prompt information, wherein the specific field is the field of GPCRs; and on the basis of the prompt information and the pre-trained large language model, obtaining an answer result, wherein the answer result is returned to a user by the pre-trained large language model. In the present application, by constructing a knowledge graph, the large language model is endowed with accurate and real-time field knowledge of GPCRs, and the ability of the large language model to answer questions of the field of GPCRs is enhanced.