DDIT: An Online Predictor for Multiple Clinical Phenotypic Drug-Disease Associations - Lu, Lu; Qin, Jiale; Chen, Jiandong; Wu, Hao; Zhao, Qiang; Miyano, Satoru; Zhang, Yaozhong; Yu, Hua; Li, Chen (Frontiers in Pharmacology, 2022)
Background: Drug repurposing provides an effective method for high-speed, low-risk drug development. Clinical phenotype-based screening exceeded target-based approaches in discovering first-in-class small-molecule drugs. However, most of these approaches predict only binary phenotypic associations between drugs and diseases, the types of drug and diseases have not been well exploited. Principally, the clinical phenotypes of a known drug can be divided into indications (Is), side effects (SEs) and contraindications (CIs). Incorporating these different clinical phenotypes of drug-disease associations (DDAs) can improve the prediction accuracy of the DDAs. Methods: We develop Drug Disease Interaction Type (DDIT), a user-friendly online predictor which supports drug repositioning by submitting known Is, SEs and CIs for a target drug of interest. The dataset for Is, SEs and CIs was extracted from PREDICT, SIDER and MED-RT, respectively. To unify the names of the drugs and diseases, we mapped their names to the Unified Medical Language System (UMLS) ontology using Rest API. We then integrated multiple clinical phenotypes into a conditional Restricted Boltzmann Machine (RBM) enabling the identification of different phenotypes of drug-disease associations, including the prediction of as yet unknown DDAs in the input. Results: By 10-fold cross-validation, we demonstrate that DDIT can effectively capture the latent features of the Drug-Disease Association Network and represents over 0.217 and over 0.072 improvement in AUC and AUPR, respectively, for predicting the clinical phenotypes of DDAs compared with the classic K-Nearest Neighbors method (KNN, including Drug-based KNN and Disease-based KNN), Random Forest and XGBoost. By conducting Leave-one-drug-class-out Cross Validation, the AUC and AUPR of DDIT demonstrated an improvement of 0.135 in AUC and 0.075 in AUPR compared to any of the other four methods. Within the top 10 predicted indications, side effects and contraindications, 7/10, 9/10, and 9/10 hit known drug-disease associations. Overall, DDIT is a useful tool for predicting multiple clinical phenotypic types of drug-disease associations. Availability and implementation: DDIT was implemented in Java, React and Mysql, and is freely available at: http://bioinfo.zju.edu.cn/DDIT.