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.- Anthology ID:
- 2023.emnlp-main.918
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 14855–14870
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.918
- DOI:
- 10.18653/v1/2023.emnlp-main.918
- Cite (ACL):
- Fangqi Zhu, Yongqi Zhang, Lei Chen, Bing Qin, and Ruifeng Xu. 2023. Learning to Describe for Predicting Zero-shot Drug-Drug Interactions. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 14855–14870, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Learning to Describe for Predicting Zero-shot Drug-Drug Interactions (Zhu et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/landing_page/2023.emnlp-main.918.pdf