rezacsedu/Drug-Drug-Interaction-Prediction
Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network
This project helps pharmacology researchers and drug discovery scientists predict potential drug-drug interactions (DDIs). It takes information from drug databases like DrugBank, PharmGKB, and KEGG, processes it into a feature-rich representation, and then uses these features to identify unforeseen negative interactions between drugs. The result is a list of predicted DDIs that can inform drug development and patient safety.
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Use this if you are a pharmacology researcher or drug discovery scientist looking to systematically predict potential drug-drug interactions using existing knowledge graph data.
Not ideal if you are a clinical practitioner seeking real-time patient-specific DDI alerts, as this is a research tool for drug discovery.
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Oct 09, 2019
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