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

arXiv: https://arxiv.org/abs/2103.10916 · HTML: https://mlsanity.com/paper/2103.10916/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 19 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [CASTER: Predicting Drug Interactions with Chemical Substructure Representation](https://mlsanity.com/paper/1911.06446/) (2019)
- [Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information](https://mlsanity.com/paper/1805.05593/) (2018)
- [A novel methodology on distributed representations of proteins using their interacting ligands](https://mlsanity.com/paper/1801.10199/) (2018)

## Abstract (arXiv)

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.