Introduction: Multifactorial complex diseases such as cancer, neurodegeneration, and
infections are poorly treated with traditional single-target therapies because biological networks are
redundant and adaptively resistant.
Methods: A comprehensive literature review was conducted to investigate the theoretical basis, design approaches (pharmacophore linking, fusing, and merging), and clinical uses of multi-target
agents using network pharmacology and systems biology.
Results: Multi-kinase inhibitors (imatinib, sunitinib, cabozantinib) approved by the Food and Drug
Administration have shown superior efficacy to traditional monotherapies due to multiple driver
inhibition; dual acetylcholinesterase and Beta-site amyloid precursor protein cleaving enzyme 1 inhibitors show enhanced neuroprotective effects against Alzheimer's disease; and β-lactam/βlactamase inhibitor combinations address drug resistance. Artificial intelligence can accelerate target
identification, and novel design technologies, such as fragment-based screening, can generate balanced polypharmacology.
Discussion: Multi-target strategies are ideal for overcoming redundancy in biological networks and
minimizing drug resistance. However, several issues remain, including the complexity of target selection, the need to achieve balanced efficacy across multiple targets, ADMET optimization, and
regulatory hurdles. Emerging technologies, such as quantum computing, precision polypharmacology based on multiomics profiling, and digital health integration, could improve target selection and
optimization.
Conclusion: Multi-target agents are no longer constrained by single-target effects; however, issues
of balanced potency, ADMET, and control still exist. The combination of AI, quantum computing,
and precision polypharmacology may enable more effective multi-target interventions to address
unmet demands in complex diseases.