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. 2023 Feb 2;40(2):276–285. doi: 10.1007/s11814-023-1377-3

Recent development of machine learning models for the prediction of drug-drug interactions

Eujin Hong 1, Junhyeok Jeon 1, Hyun Uk Kim 1,2,✉
PMCID: PMC9894510  PMID: 36748027

Abstract

Polypharmacy, the co-administration of multiple drugs, has become an area of concern as the elderly population grows and an unexpected infection, such as COVID-19 pandemic, keeps emerging. However, it is very costly and time-consuming to experimentally examine the pharmacological effects of polypharmacy. To address this challenge, machine learning models that predict drug-drug interactions (DDIs) have actively been developed in recent years. In particular, the growing volume of drug datasets and the advances in machine learning have facilitated the model development. In this regard, this review discusses the DDI-predicting machine learning models that have been developed since 2018. Our discussion focuses on dataset sources used to develop the models, featurization approaches of molecular structures and biological information, and types of DDI prediction outcomes from the models. Finally, we make suggestions for research opportunities in this field.

Keywords: Polypharmacy, Drug-Drug Interaction, Adverse Drug Reaction, Machine Learning, Featurization

Acknowledgements

This research was supported by the KAIST Key Research Institute (Interdisciplinary Research Group) Project. This work was also supported by internal fund/grant of Electronics and Telecommunications Research Institute (ETRI) [22RB1100, Exploratory and Strategic Research of ETRI-KAIST ICT Future Technology].

Footnotes

Conflict of Interest

The authors declare no conflict of interest.

Hyun Uk Kim is currently Kwon Oh-Hyun Associate Professor at Department of Chemical and Biomolecular Engineering, KAIST. His research field lies in systems biology, systems medicine and metabolic engineering. His current studies are focused on computational modeling of medically important microorganisms and selected human cells, and analysis of the biological effects of drugs. He earned his B.S. at Yonsei University, and M.S. and Ph.D. at KAIST. He joined KAIST as Assistant Professor in 2018, and has published more than 80 scientific papers.

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