Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity

Journal article

Introduction: Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved. Methods: In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements. Results: The analyses of over 45 000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2 000 000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200 000 potential DDIs. Conclusion: The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs

Authors: Rohani, N; Eslahchi, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Scientific Reports
• Volume: 9
• Issue: 1
• Pages: 13645-13645
• Publication date: 2019-09-20
• DOI: 10.1038/s41598-019-50121-3
• EISSN: 2045-2322
• ISSN: 2045-2322
• Language: English
• Keywords: Artificial neural network; Machine learning; Context (archaeology); Interaction network; Data mining; Benchmark (surveying); Artificial intelligence; Computer science; Medicine; Drug repositioning; Pharmacology; Drug-drug interaction; Drug; Similarity (geometry)