Bioinfo Chem
System biology and Infochemistry | Online ISSN 3071-4826
1
Citations
13.3k
Views
32
Articles
Volume 5 Number 1 2023
REVIEWS (Open Access)
Artificial Intelligence in Drug Repurposing: Machine Learning, Deep Learning, and Network-Based Drug Discovery
Shunqi Liu 1*
Bioinfo Chem 5 (1) 1-8 https://doi.org/10.25163/bioinformatics.5110723
Submitted: 01 January 2023 Revised: 17 February 2023 Accepted: 25 February 2023 Published: 27 February 2023
Abstract
AI-driven drug repurposing is increasingly emerging as a powerful strategy in drug discovery, addressing the high cost, long timelines, and low success rates of conventional drug development. By leveraging artificial intelligence, machine learning, and deep learning, drug repurposing is evolving from serendipitous discovery toward systematic, data-driven approaches. This review synthesizes recent advances in AI-driven drug repurposing, highlighting the integration of heterogeneous biomedical data, including genomic, transcriptomic, clinical, and pharmacological datasets. Machine learning and deep learning models demonstrate strong potential to identify novel drug–disease relationships, particularly by capturing complex, nonlinear biological interactions. Network pharmacology approaches further enhance this capability by modeling drug action within interconnected biological systems rather than isolated targets. Despite these advances, key challenges remain. Data heterogeneity, limited model interpretability, and insufficient experimental validation continue to constrain clinical translation. These limitations highlight the need for robust validation frameworks and improved data integration strategies. Overall, AI-driven drug repurposing represents a shift toward predictive and integrative drug discovery. Continued progress will depend on combining advanced computational methods with interdisciplinary validation to translate computational insights into clinically actionable therapies.
Keywords: AI-driven drug repurposing; machine learning; deep learning; network pharmacology; biomedical data integration
References
Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016). Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Molecular Pharmaceutics, 13(7), 2524–2530. https://doi.org/10.1021/acs.molpharmaceut.6b00248
Ashburn, T. T., & Thor, K. B. (2004). Drug repositioning: Identifying and developing new uses for existing drugs. Nature Reviews Drug Discovery, 3(8), 673–683. https://doi.org/10.1038/nrd1468
Bateman, A., Martin, M. J., O'Donovan, C., et al. (2017). UniProt: The universal protein knowledgebase. Nucleic Acids Research, 45(D1), D158–D169. https://doi.org/10.1093/nar/gkw1099
Bleakley, K., & Yamanishi, Y. (2009). Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics, 25(18), 2397–2403. https://doi.org/10.1093/bioinformatics/btp433
Brown, A. S., & Patel, C. J. (2017). A standard database for drug repositioning. Scientific Data, 4, 170029. https://doi.org/10.1038/sdata.2017.29
Brown, A. S., & Patel, C. J. (2018). A review of validation strategies for computational drug repositioning. Briefings in Bioinformatics, 19(1), 174–177. https://doi.org/10.1093/bib/bbw110
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039
Cheng, F., Liu, C., Jiang, J., Lu, W., Li, W., Liu, G., ... & Tang, Y. (2012). Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Computational Biology, 8(5), e1002503. https://doi.org/10.1371/journal.pcbi.1002503
Gaulton, A., Bellis, L. J., Bento, A. P., et al. (2017). The ChEMBL database in 2017. Nucleic Acids Research, 45(D1), D945–D954. https://doi.org/10.1093/nar/gkw1074
Gottlieb, A., Stein, G. Y., Ruppin, E., & Sharan, R. (2011). PREDICT: A method for inferring novel drug indications with application to personalized medicine. Molecular Systems Biology, 7, 496. https://doi.org/10.1038/msb.2011.26
Gottlieb, A., Stein, G. Y., Ruppin, E., & Sharan, R. (2011). PREDICT: A method for inferring novel drug indications with application to personalized medicine. Molecular Systems Biology, 7, 496. https://doi.org/10.1038/msb.2011.26
Jia, J., Liu, Z., Xiao, X., Liu, B., & Chou, K. C. (2015). iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. Journal of Theoretical Biology, 377, 47–56. https://doi.org/10.1016/j.jtbi.2015.04.010
Kai, Z., & Hon-Cheong, S. (2018). Drug repositioning for schizophrenia and depression/anxiety disorders: A machine learning approach leveraging expression data. IEEE Journal of Biomedical and Health Informatics.
Kanehisa, M., Furumichi, M., Tanabe, M., et al. (2017). KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Research, 45(D1), D353–D361. https://doi.org/10.1093/nar/gkw1092
Keiser, M. J., Setola, V., Irwin, J. J., Laggner, C., Abbas, A. I., Hufeisen, S. J., ... & Roth, B. L. (2009). Predicting new molecular targets for known drugs. Nature, 462(7270), 175–181. https://doi.org/10.1038/nature08506
Kim, S., Thiessen, P. A., Bolton, E. E., et al. (2016). PubChem substance and compound databases. Nucleic Acids Research, 44(D1), D1202–D1213. https://doi.org/10.1093/nar/gkv951
Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., ... & Golub, T. R. (2006). The Connectivity Map: Using gene-expression signatures to connect small molecules, genes, and disease. Science, 313(5795), 1929–1935. https://doi.org/10.1126/science.1132939
Leaman, R., Islamaj Dogan, R., & Lu, Z. (2013). DNorm: Disease name normalization with pairwise learning to rank. Bioinformatics, 29(22), 2909–2917. https://doi.org/10.1093/bioinformatics/btt473
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Li, J., & Lu, Z. (2012). Systematic identification of pharmacogenomics information from clinical trials. Journal of Biomedical Informatics, 45(5), 870–878. https://doi.org/10.1016/j.jbi.2012.02.008
Li, J., Zheng, S., Chen, B., et al. (2016). A survey of current trends in computational drug repositioning. Briefings in Bioinformatics, 17(1), 2–12. https://doi.org/10.1093/bib/bbv020
Luo, H., Li, M., Wang, S., et al. (2018). Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics, 34(11), 1904–1912. https://doi.org/10.1093/bioinformatics/bty013
Menden, M. P., Iorio, F., Garnett, M., McDermott, U., Benes, C. H., Ballester, P. J., & Saez-Rodriguez, J. (2013). Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. PLoS ONE, 8(4), e61318. https://doi.org/10.1371/journal.pone.0061318
Napolitano, F., Zhao, Y., Moreira, V. M., et al. (2013). Drug repositioning: A machine-learning approach through data integration. Journal of Cheminformatics, 5(1), 30. https://doi.org/10.1186/1758-2946-5-30
Nascimento, A. C., Prudêncio, R. B., & Costa, I. G. (2016). A multiple kernel learning algorithm for drug-target interaction prediction. BMC Bioinformatics, 17, 46. https://doi.org/10.1186/s12859-016-0890-3
Pushpakom, S., Iorio, F., Eyers, P. A., et al. (2019). Drug repurposing: Progress, challenges and recommendations. Nature Reviews Drug Discovery, 18(1), 41–58. https://doi.org/10.1038/nrd.2018.168
Schneider, G. (2018). Automating drug discovery. Nature Reviews Drug Discovery, 17(2), 97–113. https://doi.org/10.1038/nrd.2017.232
Sirota, M., Dudley, J. T., Kim, J., Chiang, A. P., Morgan, A. A., Sweet-Cordero, A., ... & Butte, A. J. (2011). Discovery and preclinical validation of drug indications using compendia of public gene expression data. Science Translational Medicine, 3(96), 96ra77. https://doi.org/10.1126/scitranslmed.3001318
Subramanian, A., Narayan, R., Corsello, S. M., et al. (2017). A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell, 171(6), 1437–1452. https://doi.org/10.1016/j.cell.2017.10.049
Wishart, D. S., Feunang, Y. D., Guo, A. C., et al. (2018). DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082. https://doi.org/10.1093/nar/gkx1037
Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., & Kanehisa, M. (2008). Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 24(13), i232–i240. https://doi.org/10.1093/bioinformatics/btn162
Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., & Kanehisa, M. (2008). Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 24(13), i232–i240. https://doi.org/10.1093/bioinformatics/btn162
Yamanishi, Y., Kotera, M., Kanehisa, M., et al. (2010). Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics, 26(12), i246–i254. https://doi.org/10.1093/bioinformatics/btq176
Recommended articles
Illuminating Biological Dark Matter: Integrating Metagenomics, Synthetic Biology, and AI to Unlock Microbial and Genomic Potential for Therapeutics and Biotechnology
An Integrated AI-Driven Framework for Maternal Resource Intelligence Shortages Across U.S. Hospital
Interpretable Machine Learning Data Modeling for Liver Disease Risk Profiling: Insights from the Indian Liver Patient Dataset
Article metrics
View details
0
Downloads
0
Citations
13
Views
0
Save
Save
0
Citation
Citation
13
View
View
0
Share
Share