Abstract

The integration of machine learning (ML) techniques with pharmacological research has advanced personalized medicine, revolutionizing drug efficacy and safety analysis. This review highlights recent developments in ML-based methods for predicting drug responses and adverse effects, emphasizing their role in tailoring treatments to individual patients. Empirical studies demonstrate the effectiveness of various ML algorithms, with deep learning models achieving 90% accuracy in predicting chemotherapy response and random forests achieving 85% accuracy in identifying adverse drug reactions. ML models, such as support vector machines (SVMs) and convolutional neural networks (CNNs) have been shown to enhance drug selection and dosing strategies by analyzing complex biomedical data. Additionally, multi-modal ML approaches combining genomic, proteomic, and clinical data provide a holistic view of patient profiles, improving predictive power. However, challenges such as data quality, model interpretability, and ethical considerations remain. This review outlines both the potential and the limitations of ML in personalized medicine and offers a forward-looking perspective on how real-time monitoring and ML integration with digital biomarkers can further optimize drug therapies.