Multi-Relational Contrastive Learning Graph Neural Network for Drug-Drug Interaction Event Prediction

With the growing variety of pharmacological compounds and the increasing need for polypharmacy, accurately predicting drug-drug interactions (DDIs) is essential to ensure both treatment efficacy and patient safety. Beneficial DDIs can enhance therapeutic outcomes. In contrast, adverse interactions may result in toxicity, reduced efficacy, or even fatality. Thus, the accurate prediction of DDIs is paramount. Building on recent advancements in graph neural network (GNN) architectures, this paper extends prior research, such as the SAGE GNN model, Graph Attention Network model, and Graph Diffusion Network model, by integrating advanced techniques such as skip connections, postprocessing layers, and optimized training methods. It start from basic GNN to buld more advanced models such as based on Adaptive Graph Diffusion model. Our experimental results shows based on evaluation on 3 different drug-drug interaction dataset that on some evaluation metric basic models outperforms the advanced ones. We have found that GCN with skip connections, GCN with NGNN and SAGE with NGNN give competent accuracy with other baseline models. The corresponding code and datasets used in this study are available on GitHub for reproducibility at: h t t p s : / / g i t h u b . c o m / k h u s h n o o d / D r u g D r u g i n t e r a c t i o n P r e d i c t i o n B a s e d O n G N N. When multiple drugs are taken simultaneously, the activity and effectiveness of one drug can be affected by another substance, usually another drug. Drug interactions are categorized into synergism and antagonism. Synergism refers to the combined use of different drugs that enhances their therapeutic effect, while antagonism occurs when two drugs interact, leading to side effects such as reduced efficacy and adverse reactions in patients. In fact, adverse reactions caused by drug interactions are quite common. For example, in recent years, many antiallergic drugs, such as loratadine and diphenhydramine, have been combined with macrolides. Serious adverse reactions can occur after the combined use of antibiotics (e.g., erythromycin). According to relevant studies, gastrointestinal reactions and skin allergies are the most common adverse effects of various macrolide antibiotics, with incidence rates as high as 48% and 26%, respectively 1 . Therefore, researchers are actively focusing on developing computational approaches to identify such interactions. The figure 1 depicts how the drug interaction prediction problem is converted to graph ML-based prediction problem. Research background In recent years, the use of polypharmacy-administering multiple drugs simultaneously-has become a common approach for managing complex diseases such as cancer. This strategy is particularly advantageous for older adults with multiple comorbidities, as it utilizes the synergistic effects of drug combinations to enhance treatment efficacy. However, unintended drug-drug interactions (DDIs) remain a significant concern, posing risks such as adverse side effects and drug toxicity. With the growing demand for multidrug therapies, the need for reliable DDI detection methods has become increasingly urgent. Traditional in vitro and in vivo approaches for identifying DDIs are both expensive and time-consuming, particularly given the vast number of possible drug combinations. Consequently, developing effective strategies for DDI prediction has become a priority in pharmaceutical research. Proactively identifying potential DDIs not only minimizes the occurrence of adverse drug reactions (ADRs) but also streamlines the drug development process, ultimately improving patient safety and therapeutic outcomes. Currently, the variety of drug interactions is vast, yet only a limited number of DDIs are identified through clinical trials and chemical experiments. As a result, some drug interactions remain unnoticed until they are widely used on the market. Often, only after patients experience adverse reactions and report them are these interactions discovered. Although this reactive approach does uncover harmful drug interactions, it comes at