Artificial Intelligence in Predictive Modeling of Drug–Drug Interactions: Advances, Applications, and Future Directions

Sudarshan Gite, Umesh Zanak, Sakshi Bharate, Pooja Rathod, Vaishali Mawal, Poonam Dalve, Shivshankar Nagrik,

doi:10.5281/zenodo.16834759

Review Paper | Open Access
Volume 02 | Issue 08 | Article Id IJSRT/250308024

Artificial Intelligence in Predictive Modeling of Drug–Drug Interactions: Advances, Applications, and Future Directions
Sudarshan Gite* ¹ Umesh Zanak ¹ Sakshi Bharate ¹ Pooja Rathod ¹ Vaishali Mawal ¹ Poonam Dalve ¹ Shivshankar Nagrik ²
¹B. Pharm, Gawande College of Pharmacy S. Kherda, Buldhana, Maharashtra, India.
²M. Pharm, Department of Pharmaceutics, Rajarshi Shahu College of Pharmacy Buldhana, Maharashtra, India

Abstract

Drug?drug interactions (DDIs) pose a significant clinical challenge, contributing to adverse drug events (ADEs), hospitalizations, and increased healthcare costs. Conventional DDI detection methods such as in vitro assays, in vivo studies, and post-marketing surveillance?provide valuable mechanistic insights but are limited by high cost, time requirements, and incomplete coverage of possible drug combinations. The rapid expansion of available pharmaceuticals has intensified the need for scalable, accurate, and timely predictive approaches. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools capable of integrating large, heterogeneous datasets spanning chemical structures, pharmacokinetics, pharmacodynamics, electronic health records (EHRs), and biomedical literature to uncover complex interaction patterns that may not be evident through traditional methods. This review provides a comprehensive overview of AI-based predictive modeling techniques for DDI identification, encompassing traditional ML algorithms, deep learning architectures, graph-based models, and natural language processing (NLP) approaches. It examines key data sources, including structured databases (e.g., DrugBank, ChEMBL, KEGG), real-world clinical data, and literature mining, and discusses strategies for data preprocessing, feature engineering, and model evaluation. Special attention is given to challenges such as data sparsity, class imbalance, lack of interpretability, and limited generalizability to novel drug combinations. Emerging solutions?including explainable AI (XAI), transfer learning, multimodal data integration, and federated learning?are highlighted as promising directions to enhance transparency, robustness, and clinical applicability. Applications of AI-driven DDI prediction span clinical decision support systems, drug development pipelines, and pharmacovigilance frameworks, with case studies demonstrating reductions in medication errors and improvements in patient safety. The review also addresses regulatory perspectives, ethical considerations, and integration challenges in clinical workflows. Ultimately, AI-based predictive modeling offers a powerful, adaptive, and scalable approach to mitigating DDI risks, supporting precision medicine, and advancing drug safety across healthcare systems.

Keywords

Drug drug interactions, Artificial intelligence, Machine learning, Deep learning, Predictive modeling , Pharmacovigilance, Explainable AI

Introduction

Drug-drug interactions (DDIs) occur when the pharmacological effect or pharmacokinetic profile of one drug is altered by the presence of another, potentially resulting in diminished efficacy or increased toxicity (1). These interactions can be pharmacokinetic, involving changes in drug absorption, distribution, metabolism, or excretion, or pharmacodynamic, where drugs influence each other's effects at the receptor or cellular level (2). DDIs represent a significant challenge in clinical practice, contributing substantially to adverse drug reactions, hospitalizations, and increased healthcare costs worldwide (3). Early and accurate prediction of DDIs is crucial for enhancing drug safety and improving patient outcomes. Proactively identifying potential interactions before clinical manifestation can prevent adverse drug events (ADEs), reduce hospital admissions, and lower the burden on healthcare systems (4). Predictive tools assist clinicians in making informed decisions during drug prescribing and facilitate regulatory agencies in evaluating drug safety profiles. Consequently, early DDI prediction plays an essential role in personalized medicine by optimizing therapeutic regimens tailored to individual patients’ medication profiles (5). Traditional approaches for DDI detection primarily rely on in vitro experiments, animal studies, and clinical trials. Although these methods provide valuable mechanistic insights, they are often time-consuming, costly, and limited in scope due to ethical and practical constraints (6). Post-marketing surveillance and spontaneous reporting systems capture real-world interaction data but suffer from underreporting and delayed identification of rare or complex interactions (7). Additionally, manual curation and rule-based systems, while effective for known DDIs, struggle to scale with the rapidly growing number of drugs and novel compounds, limiting their predictive capacity (8). The advent of artificial intelligence (AI) and machine learning (ML) has revolutionized healthcare by enabling data-driven, scalable, and efficient analysis of complex biomedical datasets (9). AI-based predictive modeling leverages large volumes of heterogeneous data—including chemical structures, biological pathways, electronic health records, and pharmacovigilance reports—to uncover hidden patterns and predict potential DDIs with greater accuracy (10). Machine learning algorithms, such as deep learning, support vector machines, and ensemble methods, have demonstrated promising results in modeling nonlinear interactions and integrating multi-modal data sources (11). These technologies not only enhance prediction accuracy but also offer interpretability and adaptability, making them powerful tools in drug safety assessment. This review aims to provide a comprehensive overview of AI-based predictive modeling approaches for drug-drug interactions. It explores the current state-of-the-art AI and ML techniques, the types of data utilized, and the challenges encountered in model development and validation. The review further discusses the integration of AI tools into clinical workflows and regulatory frameworks, emphasizing their potential to transform drug safety monitoring and personalized medicine. Finally, it highlights future directions and emerging trends in this rapidly evolving field.