Ai-Driven Polypharmacology: Designing Multi-Target Drugs for Complex Diseases

Contemporary pharmaceutical research faces significant limitations with single-target drug paradigms, particularly for multifactorial diseases such as cancer, neurodegeneration, and metabolic disorders. Polypharmacology—the deliberate design of molecules that modulate multiple disease-relevant biological targets simultaneously—offers a compelling alternative to conventional mono-target strategies. This paper investigates the convergence of artificial intelligence (AI) with polypharmacological drug design, systematically reviewing how deep learning, graph neural networks (GNNs), generative adversarial networks (GANs), and transformer-based molecular architectures are reshaping the discovery pipeline. We discuss the mechanistic basis of multi-target engagement, prominent computational frameworks for network pharmacology and target identification, and landmark AI-designed multi-target candidates across oncology, neurology, and inflammatory diseases. Benchmark comparisons reveal that transformer-based models achieve AUC-ROC scores exceeding 0.97 for multi-target affinity prediction, substantially outperforming classical machine learning baselines. We also address persistent challenges including polypharmacology selectivity, off-target toxicity prediction, and translational validation. Our analysis underscores that AI-driven polypharmacology is transitioning from a conceptual paradigm to a practical drug discovery accelerator with near-term clinical implications.