CFMKGATDDA: A new collaborative filtering and multiple kernel graph attention network-based method for predicting drug-disease associations

Drug-disease association prediction is increasingly recognized as crucial for a comprehensive understanding of the functions and mechanisms of drugs. However, the process of obtaining approval for a new drug to deal with a disease is often laborious, time-consuming and expensive. As a consequence, there is a growing interest among researchers from diverse fields in developing computational methods to identify drug-disease interactions. Thus, in this work, a new CFMKGATDDA method was proposed to unveil drug-disease associations. It firstly uses a collaborative filtering algorithm for mitigating the impact sparse associations. It secondly provides a new way to fuse multiple similarities of drugs and diseases to obtain integrated similarities for drugs and diseases. Finally, it learns drugs and diseases’ embeddings by combining multiple kernels and graph attention networks to predict high quality drug-disease associations. It attains a noticeable performance of drug-disease interaction prediction with remarkable averaged AUC and AUPR values of 0.9931 and 0.9334, respectively, on the Cdataset. When comparing on the same Cdataset, it outperforms other approaches in both metrics of AUC and AUPR. Thus, it can be regarded a useful tool for revealing drug-disease associations.