Summary of Exddi: Explaining Drug-drug Interaction Predictions with Natural Language, by Zhaoyue Sun et al.
ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language
by Zhaoyue Sun, Jiazheng Li, Gabriele Pergola, Yulan He
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed work in this paper tackles the crucial issue of predicting unknown drug-drug interactions (DDIs) by introducing natural language explanations for these predictions. This approach enables models to reveal the underlying pharmacodynamics and pharmacokinetics mechanisms alongside making the prediction, thereby increasing trust in the outputs. The authors develop various models and conduct extensive experiments using DDInter and DrugBank datasets. These models can accurately generate explanations for unknown DDIs between known drugs. Overall, this work contributes new tools to the field of DDI prediction and provides a solid foundation for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting how different medicines interact with each other. Right now, it’s hard to trust these predictions because they just tell you whether two medicines will react or not. The authors want to change this by making machines explain why certain medicines interact in certain ways. They collected explanations from other sources and built special models to test their idea. These models can correctly explain how new medicines might interact with others, which is really important for keeping people safe when taking medicine. |
