Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach

Abstract

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.

The Role of AI in Drug Discovery

“…[157] Innovative uses of CNN architecture, such as the Siamese network, have further broadened the scope of CNN applications, from reuniting missing persons with their kin to predicting drug interactions in drug discovery. [158,159] This versatility underscores the transformative potential of CNNs across multiple domains, including the prediction of ligandprotein interactions. By predicting binding affinities, CNN models contribute significantly to the refinement of scoring functions in ligand-protein interaction studies, highlighting their potential to enhance predictive capabilities in drug discovery.…”