A Survey Review of Computational Models and Their Applications in Predicting Drug-Drug Interactions

Abstract

Drug-drug interactions (DDIs) represent a substantial challenge in contemporary pharmacotherapy, especially given polypharmacy, the effects of foods, and the modification of host-microbiota systems on drugs. Although useful, existing DDI identification techniques have many constraints related to cost, time, and scalability. A literature review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore, focusing on machine learning techniques, deep neural architectures, and network-based models that integrate multi-omic, pharmacological, and clinical data. By combining chemical, biological, and clinical data into scalable computer platforms, demonstrated that artificial intelligence techniques, such as machine learning (ML) and deep learning (DL), are changing the prediction of DDI. Some notable studies, such as DeepDDI, TP-DDI, and Decagon, use approaches that successfully capture the intricate PK-PD interactions of pharmaceuticals. On the other hand, food-drug interactions and microbiome-mediated drug interactions were also successfully predicted using multimodal and graph-based models, respectively. Critical issues, such as insufficient data, class imbalance, and model interpretability, must be addressed through explainable AI and multimodal fusion techniques. The purpose of this article is to present an overview of how artificial intelligence might serve not only as a tool but also as a strategic solution for safe prescribing and tailored pharmacotherapy, hence opening up new avenues for the field of drug safety science.