Active Learning for Multi-Class Drug-Drug Interactions Prediction

The simultaneous use of multiple drugs may enhance the drug therapy process but can also increase the risk of adverse side effects. It is critical to identify the drug-drug interactions (DDIs) to avoid the possible side effects. Deep learning-based DDIs prediction models heavily rely on the quantity and quality of training data and labeled DDIs require extensive experiments, demanding significant time and resources. With the rapid discovery of new drugs, it is necessary to design a label-efficient learning method to mitigate the challenge of high DDIs annotation costs. In this study, we seamlessly integrate active learning into the multi-class DDIs prediction and present Margin-based Dynamic Cluster (MDC) active learning strategy. Extensive experiments show that our method significantly reduces annotation costs while maintaining model performance and outperforms other approaches.