This work proposes a novel approach to predicting previously unobserved side effects in drug-drug interactions (DDIs) by identifying them as Out of Distribution (OoD) samples. The rise of polypharmacy has increased both the number of drug combinations administered to patients and the risk of DDIs, many such interactions remain undocumented in clinical trials or medical records, posing a significant challenge for patient safety. While traditional DDI prediction is typically framed as a supervised machine learning problem, novel side effects lack labeled data. To address this, we introduce a hybrid approach that combines diverse drug representations with Out of Distribution (OoD) detection to capture unseen interactions. Our method integrates multiple drug embeddings generated from diverse characteristics, including biomedical texts, molecular structures, knowledge graphs and similarity profiles to generate rich latent representations that enable us to predict the presence of novel DDIs as OoD samples.