# Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information

arXiv: https://arxiv.org/abs/1805.05593 · HTML: https://mlsanity.com/paper/1805.05593/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2018.

## Built on by (most cited first, 5 of 5)

- [Fractional Denoising for 3D Molecular Pre-training](https://mlsanity.com/paper/2307.10683/) (2023)
- [Towards Unified AI Drug Discovery with Multiple Knowledge Modalities](https://mlsanity.com/paper/2305.01523/) (2023)
- [Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction Classification](https://mlsanity.com/paper/2210.13238/) (2022)
- [Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction](https://mlsanity.com/paper/2107.00596/) (2021)
- [Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach](https://mlsanity.com/paper/2103.10916/) (2021)

## Abstract (arXiv)

We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. We encode textual drug pairs with convolutional neural networks and their molecular pairs with graph convolutional networks (GCNs), and then we concatenate the outputs of these two networks. In the experiments, we show that GCNs can predict DDIs from the molecular structures of drugs in high accuracy and the molecular information can enhance text-based DDI extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set.