@article{M22B6C33E,
title = "A Novel Evaluation Method and Learning Approach for Identifying and Addressing Interaction Type Recognition Issues in Drug-Drug Interactions Prediction",
journal = "Journal of KIISE, JOK",
year = "2026",
issn = "2383-630X",
doi = "10.5626/JOK.2026.53.1.82",
author = "Youngbin Cho, Dasom Noh, Gyoung Jin Park, Minji Seo, Sunyoung Kwon",
keywords = "drug-drug interaction, interaction type recognition test, interaction type recognition, negative pair (interaction changes), DrugBank, Twosides",
abstract = "Drug-drug interaction prediction is a task aims to identify interactions between two drugs to prevent potential side effects from polypharmacy. Previous studies have employed a binary classification approach, where drug pairs and their interaction types are provided to the model to determine whether a specific interaction occurs. However, this method has limitation: the model often struggles to learn interaction types adequately, and the standard evaluation method does not highlight this issue. In this paper, we introduce a new assessment called the "Interaction Type Recognition Test" to evaluate the model's ability to identify interaction types. Additionally, we propose a learning method that incorporates negative pairs (interaction changes) to enhance model’s ability to learn these types effectively. Experiments conducted on datasets with varying structural characteristics, specifically DrugBank and Twosides, demonstrate that our proposed method significantly improves interaction type recognition performance in both datasets, validating the effectiveness of our approach in learning interaction types."
}