[Objective] This paper proposes a drug knowledge discovery method that fuses meta-path features of heterogeneous knowledge network to improve the performance of drug knowledge discovery.

[Methods] Based on different meta-paths connecting drug and target entity in heterogeneous knowledge network, the HeteSim algorithm is used to calculate the multi-dimensional semantic similarity of drug-target entity. These meta-path features are fused with drug similarity and target entity similarity features as feature inputs for machine learning models to achieve drug knowledge discovery.

[Results] The drug heterogeneous knowledge network contains 12015 nodes and 1895445 edges. Taking drug-target relation prediction as an example, the 21-dimensional HeteSim features between drug and target were calculated. The AUC value of this method achieved the highest value on the three machine learning models (XGBoost=0.993, RF=0.990, SVM=0.975). The accuracy, accuracy and F-value of this method are also higher than those of the other two comparison methods. Through literature search of 20 prediction results, it is found that some prediction results can be supported by evidence in previous literature.

[Limitations] Although PU learning strategy is used to reduce the influence of sample imbalance, some results will still be distorted.

[Conclusions] The drug knowledge discovery method proposed in this study has certain progressiveness and effectiveness, and has certain theoretical and methodological reference significance.