What Is the Role of AI in Pharmacogenomics?

Author: Rasit Dinc

Pharmacogenomics, the study of how genes affect a person's response to drugs, is undergoing a significant transformation. The integration of high-throughput “omics” technologies with advanced artificial intelligence (AI) is heralding a new era of precision medicine. While early pharmacogenetic discoveries, such as single-gene variations affecting drug metabolism, offered clear clinical benefits, it has become evident that many drug response phenotypes are governed by complex networks of genomic variants, epigenetic modifications, and metabolic pathways. This is where AI is making a substantial impact, offering the potential to unravel this complexity and provide more personalized and effective therapeutic strategies.

The Confluence of Multi-Omics and AI

The limitations of single-gene analyses have paved the way for multi-omics approaches, which incorporate data from genomics, transcriptomics, proteomics, and metabolomics to provide a holistic view of a patient's biological landscape [1]. However, the sheer volume and complexity of multi-omics data present a significant analytical challenge. This is where AI, particularly deep learning models like deep neural networks, graph neural networks, and representation learning techniques, comes into play. These advanced AI models can detect hidden patterns in high-dimensional data, fill in gaps in incomplete datasets, and even simulate treatment responses in silico [1].

AI-powered multi-omics is not just about improving predictive accuracy; it also deepens our mechanistic understanding of how gene-gene and gene-environment interactions shape therapeutic outcomes. By integrating diverse molecular layers, AI can help to create a more comprehensive and dynamic picture of drug response, moving beyond the static nature of germline variants.

Overcoming Challenges and Looking to the Future

Despite the immense potential, the widespread clinical implementation of AI-driven multi-omics in pharmacogenomics faces several hurdles. These include challenges related to data harmonization, the interpretability of complex AI models, and the need for robust regulatory oversight. Furthermore, the high cost and accessibility of multi-omics technologies, as well as the lack of diversity in genomic datasets, present significant barriers to equitable access to precision medicine [1].

Another key bottleneck is the lack of functional characterisation of many of the genetic variants identified in pharmacogenomics studies [2]. The gap between the identification and use of genetic variants for precision medicine applications is expected to diminish in the future. The increasing availability of population-scale resources that integrate electronic health records (EHRs) with genomics offers unique opportunities for identifying robust pharmacogenomics associations. In addition, recently developed AI models hold enormous potential for characterising both coding and non-coding genetic variants associated with drug response [2].

However, the field is rapidly advancing. Emerging innovations such as multi-omics foundation models, digital twins, and the use of real-world data (RWD) as a validation infrastructure are poised to accelerate clinical translation. Foundation models, inspired by large language models, can be fine-tuned for specific clinical tasks, while digital twins—virtual patient replicas—allow for in silico testing of thousands of drug options [1].

The future of pharmacogenomics lies in a paradigm shift from reactive to predictive and preventive healthcare. AI-powered multi-omics will enable clinicians to forecast disease risk, intervene earlier, and tailor interventions with unprecedented precision. This will require a concerted effort to address the existing challenges, with a focus on external validation across diverse cohorts, the development of user-centered tools, and the establishment of clear ethical and regulatory frameworks.

In conclusion, the synergy between AI and multi-omics is set to revolutionize pharmacogenomics and personalized medicine. By harnessing the power of AI to analyze complex biological data, we can move closer to a future where every patient receives the right drug at the right dose, ultimately improving therapeutic outcomes and transforming the landscape of healthcare.

References

[1] Zack, M., Stupichev, D. N., Moore, A. J., Slobodchikov, I. D., Sokolov, D. G., Trifonov, I. F., & Gobbs, A. (2025). Artificial Intelligence and Multi-Omics in Pharmacogenomics: A New Era of Precision Medicine. Mayo Clinic Proceedings: Digital Health, 3(3), 100246. https://doi.org/10.1016/j.mcpdig.2025.100246

[2] Rodriguez-Martinez, A., Kothalawala, D., Carrillo-Larco, R. M., & Poulakakis-Daktylidis, A. (2025). Artificial intelligence in precision medicine: transforming disease subtyping, medical imaging, and pharmacogenomics. Emerging Topics in Life Sciences, 8(2), 73–82. https://doi.org/10.1042/ETLS20240011