Learning to Describe for Predicting Zero-shot Drug-Drug Interactions

Fangqi Zhu, Yongqi Zhang, Lei Chen, Bing Qin, Ruifeng Xu

Submission Type: Regular Long Paper

Submission Track: Information Retrieval and Text Mining

Submission Track 2: NLP Applications

Keywords: Reinforcement Learning, Prompt Learning, Drug-Drug Interaction

Abstract:

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 https://github.com/zhufq00/DDIs-Prediction.

Submission Number: 2702