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Received September 11, 2022
Accepted December 16, 2022
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
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Recent development of machine learning models for the prediction of drug-drug interactions
1Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea 2BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Korea
ehukim@kaist.ac.kr
Korean Journal of Chemical Engineering, February 2023, 40(2), 276-285(10), 10.1007/s11814-023-1377-3
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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.
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Yu H, Dong W, Shi J, Inform. Sci., 582, 167 (2022)
