SMPR: a structure-enhanced multimodal drug‒disease prediction model for drug repositioning and cold start

Abstract

Repositioning drug‒disease relationships has always been a hot field of research. However, actual cases of biologically validated drug relocation remain very limited, and existing models have not yet fully utilized the structural information of the drug. Furthermore, most repositioning models are only used to complete the relationship matrix, and their practicality is poor when dealing with drug cold start problems. This paper proposes a structure-enhanced multimodal relationship prediction model (SMRP). SMPR is based on the SMILE structure of the drug, uses the MOL2VEC method to generate drug-embedded representations, and learns disease-embedded representations through heterogeneous network graph neural networks. Ultimately, a drug‒disease relationship matrix is constructed. In addition, to reduce the difficulty of use, SMPR also provides a cold start interface on the basis of structural similarity based on repositioning results to predict drug-related diseases simply and quickly. The repositioning ability and cold start capability of the model were verified from multiple perspectives. While the AUC and ACUPR scores of repositioning reached 99% and 61%, respectively, the AUC of the cold start method was 80%. In particular, the cold start recall indicator can reach more than 70%, which means that the SMPR is more sensitive to positive samples. Finally, case analysis was used to verify the practical value of the model, and visual analysis directly demonstrated the improvement in the structure of the model. For ease of use, we also provide local deployment of the model and packaged it into an executable program. Scientific contribution The SMPR model is a structure-enhanced multimodal learning model that focuses on the reference value of similar structures in practical docking and adds a drug embedding module. Considering that the predictive ability of the model is limited by the dataset, the model provides a cold start interface for the effective prediction of drugs that are not in the dataset. To further facilitate the use by pharmacology workers without programming knowledge and enhance its practical application value, the model was also encapsulated into a local executable program.