Computational prediction of drug-drug interactions based on drugs functional similarities

• Published in: Journal of biomedical informatics Vol. 70; pp. 54 - 64
• Main Authors: Ferdousi, Reza, Safdari, Reza, Omidi, Yadollah
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2017
• Subjects: Algorithms; Computational pharmacology; Computer Simulation; Data Mining; Drug Interactions; Drug-drug interaction; Drug-Related Side Effects and Adverse Reactions; Functional similarity; Humans; Interaction prediction; Pharmaceutical Preparations; Computational pharmacology; Interaction prediction; Functional similarity; Drug-drug interaction
• ISSN: 1532-0464; 1532-0480; 1532-0480
• DOI: 10.1016/j.jbi.2017.04.021

[Display omitted] •A similarity based method is proposed for the prediction of drug interactions.•Drug-drug interactions may occur based on common biological targets.•Similarity measures of drug interactions are based on drugs functional similarity.•Over 250,000 potential interactions were identified out of 2,394,766 drug pairs. Therapeutic activities of drugs are often influenced by co-administration of drugs that may cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and identification of DDIs are extremely vital for the patient safety and success of treatment modalities. A number of computational methods have been employed for the prediction of DDIs based on drugs structures and/or functions. Here, we report on a computational method for DDIs prediction based on functional similarity of drugs. The model was set based on key biological elements including carriers, transporters, enzymes and targets (CTET). The model was applied for 2189 approved drugs. For each drug, all the associated CTETs were collected, and the corresponding binary vectors were constructed to determine the DDIs. Various similarity measures were conducted to detect DDIs. Of the examined similarity methods, the inner product-based similarity measures (IPSMs) were found to provide improved prediction values. Altogether, 2,394,766 potential drug pairs interactions were studied. The model was able to predict over 250,000 unknown potential DDIs. Upon our findings, we propose the current method as a robust, yet simple and fast, universal in silico approach for identification of DDIs. We envision that this proposed method can be used as a practical technique for the detection of possible DDIs based on the functional similarities of drugs.