Abstract
Polypharmacology, the rational design of small molecules that act on multiple therapeutic targets, offers a transformative approach to overcome biological redundancy, network compensation, and drug resistance. This review outlines the scientific rationale for polypharmacology, highlighting its success across oncology, neurodegeneration, metabolic disorders, and infectious diseases. Emphasis is placed on how polypharmacological agents can synergize therapeutic effects, reduce adverse events, and improve patient compliance compared to combination therapies. We also explore how computational methods-spanning ligand-based modeling, structure-based docking, network pharmacology, and systems biology-enable target selection and multi-target ligand prediction. Recent advances in artificial intelligence (AI), particularly deep learning, reinforcement learning, and generative models, have further accelerated the discovery and optimization of multi-target agents. These AI-driven platforms are capable of de novo design of dual and multi-target compounds, some of which have demonstrated biological efficacy in vitro. Finally, we discuss the integration of omics data, CRISPR functional screens, and pathway simulations in guiding multi-target design, as well as the challenges and limitations of current AI approaches. Looking ahead, AI-enabled polypharmacology is poised to become a cornerstone of next-generation drug discovery, with potential to deliver more effective therapies tailored to the complexity of human disease.