Learning to Describe for Predicting Zero-shot Drug-Drug Interactions
Fangqi Zhu Yongqi Zhang Lei Chen Bing Qin Ruifeng Xu
Adverse drug-drug interactions~(DDIs) can compromise the effectiveness of
concurrent drug administration, posing a significant challenge in healthcare.
As the development of new drugs continues, the potential for unknown adverse
effects resulting from DDIs becomes a growing concern. Traditional
computational methods for DDI prediction may fail to capture interactions for
new drugs due to the lack of knowledge. In this paper, we introduce a new
problem setup as zero-shot DDI prediction that deals with the case of new
drugs. Leveraging textual information from online databases like DrugBank and
PubChem, we propose an innovative approach TextDDI with a language model-based
DDI predictor and a reinforcement learning~(RL)-based information selector,
enabling the selection of concise and pertinent text for accurate DDI
prediction on new drugs. Empirical results show the benefits of the proposed
approach on several settings including zero-shot and few-shot DDI prediction,
and the selected texts are semantically relevant. Our code and data are
available at \url{https://github.com/zhufq00/DDIs-Prediction}.