Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks
Abstract
INTRODUCTION: Drug-drug interactions (DDIs) are a major cause of preventable harm in polypharmacy and remain difficult to anticipate as formularies, indication profiles, and interaction labels evolve. Over the last few years, the DDI modeling landscape has shifted rapidly toward graph-native, multimodal, and contrastive or self-supervised learning, alongside renewed interest in extraction, decision support, and pharmacovigilance pipelines.
OBJECTIVE: This systematic literature review (SLR) synthesizes computational work on DDI prediction, event-type classification, text extraction, and safety signal detection published between 2022 and 2025. We aim to (i) organize recent methods into a feature-method taxonomy, (ii) compare their evaluation setups and reported performance, and (iii) assess progress on generalization, explainability, and clinical translation.
METHODS: Using a prespecified review protocol and PRISMA 2020 reporting guidance, we searched major bibliographic databases and screened peer-reviewed studies that proposed or evaluated computational methods for DDIs or closely related interaction tasks. Eligible work spans molecular graph and descriptor models, multimodal pharmacological representations, heterogeneous and knowledge graphs, text-based extraction and retrieval, and real-world evidence from EHRs, FAERS, and similar sources. We grouped methods into similarity and matrix-factorization baselines, conventional machine learning, deep neural architectures (CNNs,…
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