Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach
This work addresses the challenge of predicting drug-drug interactions for healthcare and pharmacology by combining heterogeneous data, representing an incremental advance over existing methods.
The paper tackled the problem of predicting drug-drug interactions by integrating multiple data sources, including drug structure images and relational representations, and demonstrated improved efficacy compared to state-of-the-art methods using standalone data types.
Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively. However, most of the approaches have focused on text data or textual representation of the drug structures. We present the first work that uses multiple data sources such as drug structure images, drug structure string representation and relational representation of drug relationships as the input. To this effect, we exploit the recent advances in deep networks to integrate these varied sources of inputs in predicting DDIs. Our empirical evaluation against several state-of-the-art methods using standalone different data types for drugs clearly demonstrate the efficacy of combining heterogeneous data in predicting DDIs.