Current - Issue
Original Article
Machine Learning-Based Prediction of Adverse Drug Reactions from Protein Interaction Profiles
Pratima Walia1
Agrimaa Singh Thakur2
1 Student, Master of Computer Applications, Indira Gandhi National Open University (IGNOU), New Delhi, India. 2 Assistant Professor, Department of Computer Science and Engineering, Baddi University of Emerging Sciences and Technology, Baddi, Himachal Pradesh, India.
Published Online: May-August 2026
Pages: 672-678
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502078References
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kernel learning and clustering methods. Comput Biol Chem. 2019;78:460–467.
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2018;287:154–162.
18. Liu M, Wu Y, Chen Y, et al. Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of
drugs. J Am Med Inform Assoc. 2012;19(e1):e28–e35.
19. Zitnik M, Agrawal M, Leskovec J. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics.
2018;34(13):i457–i466.
20. Wang CC, Shi H, Xu D, et al. Deep feature fusion for adverse drug reaction prediction. Sci Rep. 2021;11:1–12.
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2021;25(3):712–723.
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23. Zhang S, Liang M, Tang Z, et al. Graph attention network for drug adverse reaction prediction. Bioinformatics. 2022.
24. Wu Y, Xu P, Feng Y, et al. Knowledge graph-based drug side effect prediction. J Cheminform. 2022;14:1–13.
25. Cheng F, Zhao Z. Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and
genomic properties. J Am Med Inform Assoc. 2014;21(e2):e278–e286.
26. Yang X, Ye Y, Wang X, et al. Explainable deep learning for adverse drug reaction prediction. Front Pharmacol. 2022.
27. Zhao Q, Zhang X, Li S, et al. Transformer-based adverse drug reaction prediction using drug SMILES and protein sequences. J Biomed
Inform. 2023.
28. Liu J, Hu Z, Wang H, et al. Multi-task learning for correlated side effect prediction. NPJ Digit Med. 2023.
29. Chen R, Wang J, Zhu M, et al. Contrastive self-supervised learning for drug adverse event prediction. Bioinformatics. 2023.
30. Mizutani S, Pauwels E, Stoven V, Goto S, Yamanishi Y. Relating drug-protein interaction network with drug side effects. Bioinformatics.
2012;28(18):i522–i528.
31. Wishart DS, Knox C, Guo AC, et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res.
2008;36(suppl_1):D901–D906.
32. Gunther S, Kuhn M, Dunkel M, et al. SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res.
2007;36(suppl_1):D919–D922.
33. Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P. A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol.
2010;6(1):343.
34. Kim S, Chen J, Cheng T, et al. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 2021;49(D1):D1388–
D1395.
35. Drugs.com. Drug Information Online. https://www.drugs.com/. Accessed 2024.
36. Singh Thakur A, Aggarwal P. Correlation between Targeted Protein and Drug Side Effects: A Step towards the Prediction of Drug Toxicity.
SSRN Electronic Journal. 2019. doi:10.2139/ssrn.3446550. Available at: https://www.researchgate.net/publication/335894642
2. Lasser KE, Allen PD, Woolhandler SJ, et al. Timing of new black box warnings and withdrawals for prescription medications. JAMA.
2002;287(17):2215–2220.
3. National Institute on Drug Abuse (NIDA). Overdose Death Rates. 2025. https://nida.nih.gov/research-topics/trends-statistics/overdose-death-rates.
4. Giacomini KM, Krauss RM, Roden DM, et al. When good drugs go bad. Nature. 2007;446(7139):975.
5. Bender A, Scheiber J, Glick M, et al. Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects
from chemical structure. ChemMedChem. 2007;2(6):861–873.
6. Fliri AF, Loging WT, Thadeio PF, Volkmann RA. Analysis of drug-induced effect patterns to link structure and side effects of medicines.
Nat Chem Biol. 2005;1(7):389.
7. Fukuzaki M, Seki M, Kashima H, Sese J. Side effect prediction using cooperative pathways. IEEE BIBM. 2009.
8. Merle L, Laroche M-L, Dantoine T, Charmes J-P. Predicting and preventing adverse drug reactions in the very old. Drugs Aging.
2005;22(5):375–392.
9. Yan X-Y, Zhang S-W, He C-R. Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple
kernel learning and clustering methods. Comput Biol Chem. 2019;78:460–467.
10. Xian Z, Zhao X, Chen L, Lu J. A similarity-based method for prediction of drug side effects with heterogeneous information. Math Biosci.
2018;306:136–144.
11. Niu Y, Zhang W. Quantitative prediction of drug side effects based on drug-related features. Interdiscip Sci. 2017;9(3):434–444.
12. Dimitri GM, Lio P. DrugClust: a machine learning approach for drugs side effects prediction. Comput Biol Chem. 2017;68:204–210.
13. Zhang W, Yue X, Liu F, et al. A unified frame of predicting side effects of drugs by using linear neighborhood similarity. BMC Syst Biol.
2017;11(6):101.
14. Onay A, Nar F. Classifying approved and withdrawn drugs based on their targets and interactions. Turk J Chem. 2020;44:55–69.
15. Lee SI, Yasunaga H, Sugiyama M. Drug side-effect classification using data analytics. Bioinformatics. 2019.
16. Zheng K, You Z-H, Wang L, et al. MLMDA: a machine learning approach to predict and validate microRNA-disease associations by
integrating of heterogeneous information sources. J Transl Med. 2020;18:1–14.
17. Zhang W, Liu X, Chen Y, et al. Feature-derived graph regularized matrix factorization for predicting drug side effects. Neurocomputing.
2018;287:154–162.
18. Liu M, Wu Y, Chen Y, et al. Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of
drugs. J Am Med Inform Assoc. 2012;19(e1):e28–e35.
19. Zitnik M, Agrawal M, Leskovec J. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics.
2018;34(13):i457–i466.
20. Wang CC, Shi H, Xu D, et al. Deep feature fusion for adverse drug reaction prediction. Sci Rep. 2021;11:1–12.
21. Hu J, Liu J, Zheng Y, et al. Multi-label deep learning for large-scale adverse drug reaction prediction. IEEE J Biomed Health Inform.
2021;25(3):712–723.
22. Li J, Zheng S, Chen B, et al. A survey of current trends in drug repositioning. Drug Discov Today. 2016;21(4):578–580.
23. Zhang S, Liang M, Tang Z, et al. Graph attention network for drug adverse reaction prediction. Bioinformatics. 2022.
24. Wu Y, Xu P, Feng Y, et al. Knowledge graph-based drug side effect prediction. J Cheminform. 2022;14:1–13.
25. Cheng F, Zhao Z. Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and
genomic properties. J Am Med Inform Assoc. 2014;21(e2):e278–e286.
26. Yang X, Ye Y, Wang X, et al. Explainable deep learning for adverse drug reaction prediction. Front Pharmacol. 2022.
27. Zhao Q, Zhang X, Li S, et al. Transformer-based adverse drug reaction prediction using drug SMILES and protein sequences. J Biomed
Inform. 2023.
28. Liu J, Hu Z, Wang H, et al. Multi-task learning for correlated side effect prediction. NPJ Digit Med. 2023.
29. Chen R, Wang J, Zhu M, et al. Contrastive self-supervised learning for drug adverse event prediction. Bioinformatics. 2023.
30. Mizutani S, Pauwels E, Stoven V, Goto S, Yamanishi Y. Relating drug-protein interaction network with drug side effects. Bioinformatics.
2012;28(18):i522–i528.
31. Wishart DS, Knox C, Guo AC, et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res.
2008;36(suppl_1):D901–D906.
32. Gunther S, Kuhn M, Dunkel M, et al. SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res.
2007;36(suppl_1):D919–D922.
33. Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P. A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol.
2010;6(1):343.
34. Kim S, Chen J, Cheng T, et al. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 2021;49(D1):D1388–
D1395.
35. Drugs.com. Drug Information Online. https://www.drugs.com/. Accessed 2024.
36. Singh Thakur A, Aggarwal P. Correlation between Targeted Protein and Drug Side Effects: A Step towards the Prediction of Drug Toxicity.
SSRN Electronic Journal. 2019. doi:10.2139/ssrn.3446550. Available at: https://www.researchgate.net/publication/335894642
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