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Korean Journal of Chemical Engineering, Vol.40, No.2, 276-285, 2023
Recent development of machine learning models for the prediction of drug-drug interactions
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.
[References]
- Davies EA, O'Mahony MS, Br. J. Clin. Pharmacol., 80, 796, 2015
- Cho HJ, Chae J, Yoon SH, Kim DS, Front. Pharmacol., 13, 866318, 2022
- Iloanusi S, Mgbere O, Essien EJ, J. Am. Pharm. Assoc., 61, e14, 2003
- Ryu JY, Kim HU, Lee SY, Proc. Natl. Acad. Sci. U. S. A., 115, E4304, 2018
- Nyamabo AK, Yu H, Shi JY, Brief. Bioinform., 22, 1, 2021
- Pang S, Zhang Y, Song T, Zhang X, Wang X, Rodriguez-Paton A, Brief. Bioinform., 23, 1, 2022
- Lin S, Wang Y, Zhang L, Chu Y, Liu Y, Fang Y, Jiang M, Wang Q, Zhao B, Xiong Y, Wei DQ, Brief. Bioinform., 23, 1, 2022
- Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Nucleic Acids Res., 46, D1074, 2018
- Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J, Nucleic Acids Res., 34, D668, 2006
- Tatonetti NP, Ye PP, Daneshjou R, Altman RB, Sci. Transl. Med., 4, 125ra31, 2012
- Zitnik M, Agrawal M, Leskovec J, Bioinformatics, 34, i457, 2018
- Ioannidis VN, Song X, Manchanda S, Li M, Pan X, Zheng D, Ning X, Zeng X, Karypis G, (2021).
- Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M, Nucleic Acids Res., 38, D355, 2010
- Asada M, Miwa M, Sasaki Y, Bioinformatics, 37, 1739, 2021
- Lee K, Lee S, Jeon M, Choi J, Kang J, 2012 IEEE Int. Conf. Bioinf. Biomed., 1, 2012
- Ryu S, Kwon Y, Kim WY, Chem. Sci., 10, 8438, 2019
- Elbadawi M, Gaisford S, Basit AW, Drug Discov. Today, 26, 769, 2021
- Weininger D, J. Chem. Inf. Comput. Sci., 28, 31, 1988
- Jeon J, Kang S, Kim HU, Nat. Prod. Rep., 38, 1954, 2021
- Rogers D, Hahn M, J. Chem. Inf. Model., 50, 742, 2010
- Deng Y, Xu X, Qiu Y, Xia J, Zhang W, Liu S, Bioinformatics, 36, 4316, 2020
- Feng YH, Zhang SW, Shi JY, BMC Bioinformatics, 21, 419, 2020
- Moriwaki H, Tian YS, Kawashita N, Takagi T, J. Cheminform., 10, 4, 2018
- Cao DS, Xu QS, Hu QN, Liang YZ, Bioinformatics, 29, 1092, 2013
- Kipf TN, Welling M, arXiv preprint arXiv:1609.02907 (2016).
- Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y, arXiv preprint arXiv.1710.10903 (2017).
- Feng YH, Zhang SW, Zhang QQ, Zhang CH, Shi JY, Anal. Biochem., 646, 114631, 2022
- Chen Y, Ma T, Yang X, Wang J, Song B, Zeng X, Bioinformatics, 37, 2651, 2021
- Yu Y, Huang K, Zhang C, Glass LM, Sun J, Xiao C, Bioinformatics, 37, 2988, 2021
- Ren ZH, Yu CQ, Li LP, You ZH, Guan YJ, Wang XF, Pan J, Brief. Funct. Genomics, 21, 216, 2022
- Lee G, Park C, Ahn J, BMC Bioinformatics, 20, 415, 2019
- Kim E, Nam H, J. Cheminform., 14, 9, 2022
- Chatr-Aryamontri A, Oughtred R, Boucher L, Rust J, Chang C, Kolas NK, O'Donnell L, Oster S, Theesfeld C, Sellam A, Stark C, Breitkreutz BJ, Dolinski K, Tyers M, Nucleic Acids Res., 45, D369, 2017
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Nat. Genet., 25, 25, 2000
- C. The Gene Ontology, Nucleic Acids Res., 45, D331, (2017).
- Hao X, Chen Q, Pan H, Qiu J, Zhang Y, Yu Q, Han Z, Du X, Granular Computing, 8, 67, 2023
- Trouillon T, Welbl J, Riedel S, Gaussier E, Bouchard G, ICML, 48, 2071, 2016
- Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Adv. Neural Inf. Process. Syst., 32, 1, 2019
- Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, arXiv preprint arXiv:1603.04467 (2016).
- Zhang W, Chen Y, Liu F, Luo F, Tian G, Li X, BMC Bioinformatics, 18, 18, 2017
- Zhang W, Liu Y, Wang L, Zhou J, Du J, Goh RSM, ICCCRI, 18, 2017
- Chen X, Liu X, Wu J, Methods, 179, 47, 2020
- Himmelstein DS, Baranzini SE, PLoS Comput. Biol., 11, e1004259, 2015
- Zhang HR, Min F, Shi B, Inform. Sci., 378, 444, 2017
- Yue X, Wang Z, Huang J, Parthasarathy S, Moosavinasab S, Huang Y, Lin SM, Zhang W, Zhang P, Sun H, Bioinformatics, 36, 1241, 2020
- Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE, Lu X, Gould J, Davis JF, Tubelli AA, Asiedu JK, Lahr DL, Hirschman JE, Liu Z, Donahue M, Julian B, Khan M, Wadden D, Cell, 171, 1437, 2017
- Nyamabo AK, Yu H, Liu Z, Shi JY, Brief. Bioinform., 23, 1, 2022
- He C, Liu Y, Li H, Zhang H, Mao Y, Qin X, Liu L, Zhang X, BMC Bioinformatics, 23, 224, 2022
- Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P, Mol. Syst. Biol., 6, 343, 2010
- Kuhn M, Letunic I, Jensen LJ, Bork P, Nucleic Acids Res., 44, D1075, 2016
- Zhuang L, Wang H, Li W, Liu T, Han S, Zhang H, Soft Computing, 26, 11795, 2022
- Yu H, Dong W, Shi J, Inform. Sci., 582, 167, 2022
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