Papers › Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information
Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information
Masaki Asada, Makoto Miwa, Yutaka Sasaki
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.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Drug–drug Interaction Extraction | DDI extraction 2013 corpus | MOL+CNN | F1 | 0.7255 | #6 of 10 | Archive leaderboard | report |
| Drug–drug Interaction Extraction | DDI extraction 2013 corpus | MOL+CNN | Micro F1 | 72.55 | #6 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections