Author(s)

Yi Wan ¹

Affiliation(s)

¹ Faculty of Science, University of Technology Sydney, Sydney, 2007, Australia

Corresponding Author

Yi Wan

ABSTRACT

Drug repurposing increasingly relies on transcriptomic evidence to identify new therapeutic uses for existing drugs or drug-like compounds. This review examines how AI-assisted gene expression signature reversal contributes to candidate prioritization in drug repurposing. The approach compares disease-associated gene expression signatures with drug-induced transcriptional profiles, using resources such as the Connectivity Map and LINCS as the data foundation. Signature reversal is valuable because it offers a biologically interpretable ranking logic: compounds that oppose disease-associated expression changes may be prioritized for further study. The reviewed literature suggests that this logic is useful, but uneven. Some studies provide evidence that reversal scores can relate to drug efficacy, whereas validation-focused work also shows that reversal signals may be confounded by broad anti-proliferative effects. AI and machine learning extend this framework by supporting high-dimensional expression analysis, transcriptional response prediction, expression ranking, and context-aware prioritization. Recent deep learning approaches highlight the importance of dose and cellular context when predicting drug-induced responses. Overall, AI-assisted signature reversal is most defensible as a candidate triage strategy rather than as evidence of efficacy. Its value depends on combining reversal scores with pathway interpretation, biological context, and targeted follow-up testing.

KEYWORDS

Drug repurposing; Gene expression signature reversal; Connectivity Map; Artificial intelligence; Candidate prioritization

CITE THIS PAPER

Yi Wan. AI-Assisted Gene Expression Signature Reversal for Candidate Prioritization in Drug Repurposing. MEDS Clinical Medicine (2026). Vol. 7, No. 3, 110-116. DOI: http://dx.doi.org/10.23977/medsc.2026.070315.

REFERENCES

[1] Zhao, Y., Chen, X., Chen, J., & Qi, X. (2023). Decoding Connectivity Map-based drug repurposing for oncotherapy. Briefings in Bioinformatics, 24(3), bbad142. https://doi.org/10.1093/bib/bbad142
[2] Kwon, O. S., Kim, W., Cha, H. J., & Lee, H. (2019). In silico drug repositioning: From large-scale transcriptome data to therapeutics. Archives of Pharmacal Research, 42(10), 879–889. https://doi.org/10.1007/s12272-019-01176-3
[3] Fisher, J. L., Wilk, E. J., Oza, V. H., Gary, S. E., Howton, T. C., Flanary, V. L., Clark, A. D., Hjelmeland, A. B., & Lasseigne, B. N. (2024). Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates. FEBS Open Bio, 14(5), 803–830. https://doi.org/10.1002/2211-5463.13796
[4] Chen, B., Ma, L., Paik, H., Sirota, M., Wei, W., Chua, M. S., So, S., & Butte, A. J. (2017). Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets. Nature Communications, 8, 16022. https://doi.org/10.1038/ncomms16022
[5] Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J. P., Subramanian, A., Ross, K. N., Reich, M., Hieronymus, H., Wei, G., Armstrong, S. A., Haggarty, S. J., Clemons, P. A., Wei, R., Carr, S. A., Lander, E. S., & 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
[6] Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., Lahr, D. L., Hirschman, J. E., Liu, Z., Donahue, M., Julian, B., Khan, M., Wadden, D., Smith, I. C., Lam, D., ... Golub, T. R. (2017). A next generation Connectivity Map: L1000 platform and the first 1,000,000 profiles. Cell, 171(6), 1437–1452.e17. https://doi.org/10.1016/j.cell.2017.10.049
[7] Duan, Q., Reid, S. P., Clark, N. R., Wang, Z., Fernandez, N. F., Rouillard, A. D., Readhead, B., Tritsch, S. R., Hodos, R., Hafner, M., Niepel, M., Sorger, P. K., Dudley, J. T., Bavari, S., Panchal, R. G., & Ma’ayan, A. (2016). L1000CDS²: LINCS L1000 characteristic direction signatures search engine. npj Systems Biology and Applications, 2, 16015. https://doi.org/10.1038/npjsba.2016.15
[8] Rodriguez, S., Hug, C., Todorov, P., Moret, N., Boswell, S. A., Evans, K., Zhou, G., Johnson, N. T., Hyman, B. T., Sorger, P. K., Albers, M. W., & Sokolov, A. (2021). Machine learning identifies candidates for drug repurposing in Alzheimer’s disease. Nature Communications, 12(1), 1033. https://doi.org/10.1038/s41467-021-21330-0
[9] Zhu, J., Wang, J., Wang, X., Gao, M., Guo, B., Gao, M., Liu, J., Yu, Y., Wang, L., Kong, W., An, Y., Liu, Z., Sun, X., Huang, Z., Zhou, H., Zhang, N., Zheng, R., & Xie, Z. (2021). Prediction of drug efficacy from transcriptional profiles with deep learning. Nature Biotechnology, 39(11), 1444–1452. https://doi.org/10.1038/s41587-021-00946-z
[10] Pham, T. H., Qiu, Y., Liu, J., Zimmer, S., O’Neill, E., Xie, L., & Zhang, P. (2022). Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing. Patterns, 3(4), 100441. https://doi.org/10.1016/j.patter.2022.100441
[11] Wu, Y., Liu, Q., Qiu, Y., & Xie, L. (2022). Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized Alzheimer’s disease drug repurposing. PLOS Computational Biology, 18(8), e1010367. https://doi.org/10.1371/journal.pcbi.1010367
[12] Koudijs, K. K. M., Böhringer, S., & Guchelaar, H. J. (2023). Validation of transcriptome signature reversion for drug repurposing in oncology. Briefings in Bioinformatics, 24(1), bbac490. https://doi.org/10.1093/bib/bbac490
[13] Cong, Y., Shintani, M., Imanari, F., Osada, N., & Endo, T. (2022). A new approach to drug repurposing with two-stage prediction, machine learning, and unsupervised clustering of gene expression. OMICS: A Journal of Integrative Biology, 26(6), 339–347. https://doi.org/10.1089/omi.2022.0026
[14] Musa, A., Ghoraie, L. S., Zhang, S. D., Glazko, G., Yli-Harja, O., Dehmer, M., Haibe-Kains, B., & Emmert-Streib, F. (2018). A review of Connectivity Map and computational approaches in pharmacogenomics. Briefings in Bioinformatics, 19(3), 506–523. https://doi.org/10.1093/bib/bbw112
[15] Mousavi, S. Z., Rahmanian, M., & Sami, A. (2020). A connectivity map-based drug repurposing study and integrative analysis of transcriptomic profiling of SARS-CoV-2 infection. Infection, Genetics and Evolution, 86, 104610. https://doi.org/10.1016/j.meegid.2020.104610

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