Integrating Network Pharmacology and AI for Multi-Target Drug Discovery in Complex Diseases

Abstract

The conventional "one drug, one target" paradigm has proven inadequate for treating complex, multifactorial diseases like cancer, Alzheimer's disease, and autoimmune disorders. These conditions arise from dysregulated biological networks, necessitating a paradigm shift toward systems-level therapeutic strategies. This article reviews the transformative integration of network pharmacology and artificial intelligence (AI) for multi-target drug discovery. Network pharmacology provides the foundational framework by constructing and analyzing drugtarget-disease networks to identify key intervention points, such as hub and bottleneck proteins. AI, particularly machine learning and graph neural networks, acts as an indispensable catalyst, enabling the scalable analysis of multi-omics data, the prediction of novel drug-target interactions, and the de novo design of multi-target ligands. We outline the core principles of this integrated workflow, from constructing dynamic network models to employing explainable AI for interpretable predictions. The power of this approach is demonstrated through its application across diverse therapeutic areas: deconvoluting oncogenic signaling networks in pancreatic cancer, elucidating the polypharmacology of natural products in inflammatory diseases, and addressing the shared network perturbations in neurodegenerative and metabolic disorders by integrating gut microbiome data. While challenges in data integration, model interpretability, and translational validation persist, the confluence of network pharmacology and AI charts a new roadmap for drug discovery. This synergy promises to accelerate the development of more effective, resilient, and personalized multi-target therapies, ultimately offering a powerful strategy to restore homeostasis in complex disease networks.