Transforming Drug Therapy with Deep Learning: The Future of Personalized Medicine

Abstract

Personalized medicine represents a paradigm shift in healthcare, aiming to tailor treatment strategies to the unique genetic, environmental, and lifestyle characteristics of individual patients. This approach holds immense potential for improving therapeutic efficacy and minimizing adverse drug reactions. With the rapid advancement of artificial intelligence, deep learning has emerged as a transformative tool in pharmacology, enabling precise modeling of complex biological data and uncovering hidden patterns in patient-specific information. This study investigates the application of deep learning techniques – such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer architectures, and Generative Adversarial Networks (GANs) – in optimizing personalized treatment strategies. Using a diverse dataset comprising electronic health records (EHRs), genomic sequences, and clinical indicators, we developed and trained deep learning models for tasks including drug response prediction, biomarker identification, and adverse drug reaction (ADR) forecasting. Among the models evaluated, Transformer-based architectures demonstrated superior performance, achieving an accuracy of 91.2% and an AUC-ROC of 0.92 in drug response prediction tasks. Moreover, the integration of deep learning models into the treatment pipeline resulted in a 20–30% improvement in drug-patient matching efficiency compared to traditional statistical methods. The findings underscore the potential of AI-powered systems to enhance clinical decision-making and enable precision pharmacotherapy. However, challenges such as data privacy, model interpretability, and regulatory compliance remain critical barriers to widespread adoption. The study also explores future directions, including the implementation of explainable AI (XAI) and federated learning, to address these limitations and facilitate the integration of deep learning into routine clinical practice.

Keywords

deep learning - personalized medicine - pharmacology - drug response prediction - AI in healthcare

Publication History

Received: 25 June 2025

Accepted: 04 August 2025

Article published online:
29 August 2025

• References

• 1 Papadakis, Georgios Z, Karantanas, Apostolos H, Tsikankis, Manolis, Tsatsakis, Aristidis, Spandidos, Demetrios, and Marias, Kostas “Deep learning opens new horizons in personalized medicine (Review)”. Spandidos Publishing; 2019.
• 2 Wang H, Pujos-Guillot E, Comte B. et al. Deep learning in systems medicine. Brief Bioinform. 2021; 22: 1543-1559 PMID: 33197934; PMCID: PMC8382976
• 3 Yang S, Zhu F, Ling X. et al. “Intelligent Health Care: Applications of Deep Learning in Computational Medicine”. Frontiers Media; 2021.
• 4 Hassan M, Awan FM, Naz A. et al. “Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review”. Multidisciplinary Digital Publishing Institute; 2022.
• 5 Chiu YC, Chen HH, Gorthi A. et al. “Deep learning of pharmacogenomics resources: moving towards precision oncology”. None 2019;
• 6 Rezayi S, Rostam Niakan Kalhori S, Saeedi S. 2022. “Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review”. Hindawi Publishing Corporation;
• 7 Martorell-Marugán J, Tabik S, Benhammou Y. et al. “Deep Learning in Omics Data Analysis and Precision Medicine”. Computational Biology 2019;
• 8 Hoffman GE, Bendl J, Girdhar K. et al. “Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification”. Oxford University Press; 2019.
• 9 Singh S, Kumar R, Payra S. et al. “Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery”. Cureus, Inc. 2023;
• 10 Serrano DR, Luciano FC, Anaya BJ. et al. Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics 2024; 16: 1328 PMID: 39458657; PMCID: PMC11510778
• 11 Visan AI, Negut I. “Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery”. Multidisciplinary Digital Publishing Institute; 2024.
• 12 Naderi M, Lemoine JM, Govindaraj RG. et al. “Binding site matching in rational drug design: algorithms and applications”. Oxford University Press; 2018.
• 13 Rifaiolu, Ahmet Sreyya, Ata, Heval, Martin, Maraetin-Atalay, Rengl, Atalay, Volkan, and Doan, Tunca “Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases”. Oxford University Press; 2018.
• 14 Xu Y, Lin K, Wang S. et al. “Deep learning for molecular generation.”. Future Medicinal Chemistry 2019;
• 15 Salvi, Massimo, Loh, Hui Wen, Seoni, Silvia, Barua, Prabal Datta, Garca, Salvador, Molinari, Filippo, and Acharya, U. Rajendra 2023. “Multi-modality approaches for medical support systems: A systematic review of the last decade”. Elsevier BV;
• 16 Rajkomar A, Oren E, Chen K. et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018; 1: 18 PMID: 31304302; PMCID: PMC6550175
• 17 Trivizakis E, Papadakis GZ, Souglakos I. et al. “Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)”. Spandidos Publishing; 2020.
• 18 Vaz JM, Balaji S. “Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics”. Springer Science+Business Media; 2021.
• 19 Preuss K, Thach N, Liang X. et al. Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Cancers 2022; 14: 1654
• 20 Landi I, Glicksberg BS, Lee HC. et al. “Deep representation learning of electronic health records to unlock patient stratification at scale”. Nature Portfolio; 2020.
• 21 Lu J, Deng K, Zhang X. et al. “Neural-ODE for pharmacokinetics modeling and its advantage to alternative machine learning models in predicting new dosing regimens”. Cell Press; 2021.
• 22 Lin E, Kuo PH, Liu YL. et al. “A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers”. Frontiers Media; 2018.
• 23 Cui Y, Zhang J, Li Z. et al. A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study. EClinicalMedicine 2022; 46: 101348 PMID: 35340629; PMCID: PMC8943416
• 24 Visco V, Izzo C, Mancusi C. et al. Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve. Journal of Cardiovascular Development and Disease 2023; 10: 74
• 25 Kalinin AA, Higgins GA, Reamaroon N. et al. "Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification". Future Medicine 2018;
• 26 Germovsek E, Barker CIS, Sharland M. et al. “PharmacokineticPharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance”. Adis, Springer Healthcare; 2018.
• 27 Fan K, Cheng L, Li L. “Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects”. Oxford University Press; 2021.
• 28 Lee G, Park C, Ahn J. “Novel deep learning model for more accurate prediction of drug-drug interaction effects”. BioMed Central; 2019.
• 29 Doan, Minh, Vorobjev, Ivan A., Rees, Paul, Filby, Andrew, Wolkenhauer, Olaf, Goldfeld, Anne E., Lieberman, Judy, Barteneva, Natasha S., Carpenter, Anne E., and Hennig, Holger “Diagnostic Potential of Imaging Flow Cytometry”. Elsevier BV; 2018.
• 30 Yang Z, Ren M. “Advantages of Hybrid Deep Learning Frameworks in Applications with Limited Data”. None 2018;
• 31 Nguyen T, Nguyen GTT, Nguyen T. et al. “Graph Convolutional Networks for Drug Response Prediction”. Institute of Electrical and Electronics Engineers 2021;
• 32 Sheller MJ, Edwards B, Reina GA. et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep 10 2020; 12598
• 33 Zhang, Yu, Tio, Peter, Leonardis, Ale, and Tang, Ke “A Survey on Neural Network Interpretability”. Institute of Electrical and Electronics Engineers 2021;
• 34 Sarkar C, Das B, Rawat VS. et al. “Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development”. Multidisciplinary Digital Publishing Institute; 2023.
• 35 MacRae CA, Peterson RT. “Zebrafish as a Mainstream Model for In Vivo Systems Pharmacology and Toxicology”. Annual Reviews; 2022.
• 36 Zhou S, Zhao J, Zhang L. “Application of Artificial Intelligence on Psychological Interventions and Diagnosis: An Overview”. Frontiers Media; 2022.