# Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks

arXiv: https://arxiv.org/abs/1911.06356 · HTML: https://mlsanity.com/paper/1911.06356/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Exploring QSAR Models for Activity-Cliff Prediction](https://mlsanity.com/paper/2301.13644/) (2023)

## Abstract (arXiv)

Predicting and discovering drug-drug interactions (DDIs) is an important problem and has been studied extensively both from medical and machine learning point of view. Almost all of the machine learning approaches have focused on text data or textual representation of the structural data of drugs. We present the first work that uses drug structure images as the input and utilizes a Siamese convolutional network architecture to predict DDIs.