Multi-target drug discovery has become an essential strategy for treating complex diseases involving multiple molecular pathways. Traditional single-target approaches often fall short in addressing the multifactorial nature of conditions such as cancer and neurodegenerative disorders. With the rise in large-scale biological data and algorithmic advances, machine learning (ML) has emerged as a powerful tool to accelerate and optimize multi-target drug development. This review presents a comprehensive overview of ML techniques, including advanced deep learning (DL) approaches like attention-based models, and highlights their application in multi-target prediction, from traditional supervised learning to modern graph-based and multi-task learning frameworks. We highlight real-world applications in oncology, central nervous system disorders, and drug repurposing, showcasing the translational potential of ML in systems pharmacology. Major challenges are discussed, such as data sparsity, lack of interpretability, limited generalizability, and integration into experimental workflows. We also address ethical and regulatory considerations surrounding model transparency, fairness, and reproducibility. Looking forward, we explore promising directions such as generative modeling, federated learning, and patient-specific therapy design. Together, these advances point toward a future of precision polypharmacology driven by biologically informed and interpretable ML models. This review aims to provide researchers and practitioners with a roadmap for leveraging ML in the development of safer and more effective multi-target therapeutics.
Bi et al. (2025) studied this question.