Acta pharmaceutica Sinica. BOct 24, 2025

New insights into Alzheimer's disease research using artificial intelligence and biological systems analysis

Updated Apr 22, 2026

Abstract

Artificial intelligence, computational biology, and systems biology may enhance understanding of Alzheimer's disease pathology.

Alzheimer's disease is marked by cognitive decline and the presence of amyloid-beta aggregates and neurofibrillary tangles.
Current treatments aimed at reducing amyloid-beta production and inhibiting tau protein aggregation have not achieved expected clinical outcomes.
AI and related methodologies can analyze large datasets to identify complex patterns associated with Alzheimer's disease.
Imaging omics may facilitate early detection of Alzheimer's disease.
Drug discovery efforts include methods like lecanemab, while complementary therapies such as phototherapy are also explored.

Simplified

Alzheimer's disease (AD) is characterized by cognitive and functional deterioration, with pathological features such as amyloid-beta (A) aggregates in the extracellular spaces of parenchymal neurons and intracellular neurofibrillary tangles formed by the hyperphosphorylation of tau protein. Despite a thorough investigation, current treatments targeting the reduction of Aproduction, promotion of its clearance, and inhibition of tau protein phosphorylation and aggregation have not met clinical expectations, posing a substantial obstacle in the development of drugs for AD. Recently, artificial intelligence (AI), computational biology (CB), and systems biology (SB) have emerged as promising methodologies in AD research. Their capacity to analyze extensive and varied datasets facilitates the identification of intricate patterns, thereby enriching our comprehension of AD pathology. This paper provides a comprehensive examination of the utilization of AI, CB, and SB in the diagnosis of AD, including the use of imaging omics for early detection, drug discovery methods such as lecanemab, and complementary therapies like phototherapy. This review offers novel perspectives and potential avenues for further research in the realm of translational AD studies. β β

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The authors have no conflicts of interest to declare.

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