Prediction of Drug–Drug Interactions Based on Artificial Intelligence: A Systematic Literature Review

Abstract

The comprehensive knowledge about the simultaneous use of multiple drugs to treat a disease is essential for the medical community to determine the best decisions for patient health. The use of various drugs at the same time to treat a disease can result in drug–drug interaction, raising the possibility of serious side effects. This study conducted a systematic literature review that describes the declarative information about drug–drug interactions, including the research papers from 2019 to 2025. The study focused on significant areas that can enhance modern research in drug–drug interactions, which were not included in previous studies. It is composed of artificial intelligence techniques, particularly those based on machine learning and deep learning for predicting drug–drug interactions. The PRISMA-based flow chart concept is used in the literature review stage. After a thorough review of the research papers, 33 studies were chosen. This work presents four research questions that were addressed and answered by the obtained results. The study found that the drug–drug interaction trend increased starting from 2021. It also found that deep learning models and their hybrid frameworks are the most commonly used. It was also observed that most studies used DrugBank and TWOSIDE data repositories. The findings also reveal that the F1-score is the most frequent evaluation measure. It found that there is a bridge to validate the model’s performance on a real-time clinical dataset, thereby assessing its true power. The research findings highlight significant study trends and knowledge gaps in the medical industry.

Received: 31 August 2025 | Revised: 9 January 2026 | Accepted: 26 March 2026

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Faisal Asad ur Rehman: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Abdulkarim Kanaan Jebna: Validation, Investigation, Visualization, Supervision, Project administration. Touqeer Ahmad: Methodology, Resources. Arif Ur Rahman: Data curation, Writing – review & editing, Project administration. Fasee Ullah: Formal analysis, Writing – review & editing, Visualization.